The article reviews Flink 1.12's major enhancements, including comprehensive SQL capabilities, deep integration with Hive via catalog and streaming support, and a practical code example that demonstrates how to write streaming data into Hive tables while handling partition commits and small‑file merging.
This article, presented by senior data platform engineer Mao Xiangyi of TAL Education, details the design and implementation of the company’s real‑time T‑Streaming platform, covering its three‑layer data architecture, batch‑stream integration techniques, ODS layer real‑timeization, Flink SQL development workflow, hybrid‑cloud deployment, and a case study of K‑12 renewal reporting.
This talk by Ba Xueyu, a senior big data platform engineer at Zhongyuan Bank, outlines the background, architecture, and engineering practices of a real‑time financial data lake, highlighting its open, timely, and integrated design, streaming platform implementation, and use cases such as anti‑fraud and real‑time BI.
Facing a massive 1500‑node Flink 1.4.2 cluster handling over 12,000 tasks and 30 trillion daily events, we migrated to Flink 1.10, detailing new DDL/Catalog support, SQL enhancements, memory tuning, compatibility patches, extensive testing, and engine optimizations such as task‑load metrics and balanced sub‑task scheduling.
This article explains how to diagnose and optimize Flink jobs by first examining metrics, then checking resource allocation, analyzing throughput and back‑pressure, and finally tuning JVM settings, while providing concrete configuration examples and practical tips for big‑data practitioners.
This article explains how to use the Cloud Native Prometheus ecosystem—including Prometheus Server, exporters, Pushgateway, Alertmanager and Grafana—to collect, store, query and visualize Flink job metrics, providing a complete monitoring solution for production clusters.
This article presents a detailed case study of NetEase Cloud Music’s real‑time analytics platform built on Kafka and Flink, covering background, architectural choices, platform‑level design, operational challenges, solutions such as the Magina framework, and a Q&A on reliability and monitoring.
At Youzan, Flink real‑time tasks running on Kubernetes are optimized by daily GC‑log memory analysis and Kafka‑throughput monitoring, which compute recommended heap sizes and parallelism adjustments to eliminate over‑provisioned CPU and memory, automate alerts, and pave the way for fully automated resource tuning.
DiDi upgraded its massive real‑time computing engine from Flink 1.4.2 to Flink 1.10, implementing a transparent migration across 1500 machines, adding native DDL, binary rows, MiniBatch, improved scheduling and window functions, and establishing a rigorous testing pipeline that achieved 99.9 % compatibility while preventing OOM issues.
This article recounts the author’s journey building a real‑time data warehouse using Flink, Kafka, Redis, and ClickHouse, describing the initial batch‑oriented setup, successive architectural evolutions, challenges with wide tables and dimension data, and the final OLAP‑centric solution with secondary caching.
This guide explains how to integrate Flink 1.12 with Hive using HiveCatalog, covering required dependencies, Blink planner configuration, SQL dialect switching, Hive UDF support, temporal table joins, and provides complete code snippets for a streaming‑batch unified data warehouse solution.
This article presents a detailed collection of 2021 Flink interview questions covering checkpoint mechanisms, watermarks, state backends, join types, fault tolerance, resource configuration, and recent Flink 1.10 features, providing concise explanations and code examples for each topic.
This article describes how NetEase built a real‑time log intelligent classification system using Flink and AI algorithms, detailing the challenges of massive log volumes, the Drain template‑extraction method, algorithm workflow, performance results, and a practical case study that demonstrates reduced alert storms and faster issue diagnosis.
This article describes how a fast‑growing company built a layered real‑time data warehouse on Flink, detailing the evolution from a simple 1.0 pipeline to a 2.0 architecture with ODS, DWD and ADS layers, dimension joins, exactly‑once sinks, HDFS partitioning, monitoring, and future improvements.
This article provides a step‑by‑step guide on configuring MySQL binlog, installing and deploying Oracle GoldenGate, extracting changes, converting them to JSON, and streaming the data into Kafka for real‑time processing, complete with code snippets and verification procedures.
This article provides a step‑by‑step guide on configuring the MySQL CDC connector for Flink, covering Maven and SQL client dependencies, MySQL user setup, connector options, table creation via SQL and Stream API, key features, common issues, and practical troubleshooting tips.
This article examines the pervasive small‑file issue in big‑data environments, explains its impact on storage and processing performance, and presents a comprehensive set of solutions—including file merging, Hadoop archives, SequenceFiles, HBase, CombineFileInputFormat, and Spark/Flink strategies—to mitigate metadata overhead and improve I/O efficiency.
This article details how to migrate Spark Streaming jobs to Flink Streaming by creating custom SourceFunction and SinkFunction implementations, including a RocketMQ source connector and an HBase sink, with code examples, configuration tips, and discussion of checkpointing and watermark handling.
At Youzan’s Big Data Technology Salon, over 100 attendees heard leaders from Youzan, NetEase Yishu, and Didi discuss cost governance, Apache Iceberg data lakes, large‑scale Flink real‑time computing, and data‑driven growth strategies, highlighting practical implementations, savings of millions and tools for merchant empowerment.
This tutorial introduces Apache Zeppelin, explains REPL and Jupyter concepts, outlines its core features and project structure, and provides step‑by‑step instructions for installing Zeppelin, creating notebooks, and connecting to databases, Spark, and Flink with practical code examples.
This article explains how to leverage Flinkx and Flink Stream API to create a unified data‑sync task that extracts data from sharded MySQL tables, splits the workload, and pushes it to an MQ cluster, while detailing the underlying InputFormat and Reader architecture.
This article provides a comprehensive overview of data middle platform concepts, covering data aggregation, collection tools, development modules, job scheduling, baseline control, heterogeneous storage, permission management, real‑time and offline processing, governance, services, and implementation details for building robust big‑data solutions.
This article explains why Kerberos is needed for Hadoop clusters, details the Kerberos authentication workflow, and provides step‑by‑step instructions for configuring Flink to run on a Kerberos‑protected YARN environment using delegation tokens or keytab files, along with proxy‑user settings.
This article explains how to build a real‑time data processing platform using Flink, covering the Lambda architecture, design approaches, SQL and custom‑Jar task definitions, UI drag‑and‑drop, cluster resource management on Yarn and Kubernetes, submission modes, scheduling, permission and metadata handling, logging, and monitoring with Prometheus and Grafana.
This article explains Flink’s architecture and details how to run Flink on Kubernetes using both Standalone and native modes, covering Session and Per‑Job deployment, required ConfigMaps, Deployments, Services, and command‑line steps for creating, submitting, and deleting Flink jobs.
The Youzan Big Data Technology Salon brought together Youzan, NetEase and Didi to share practical approaches for cutting data‑infrastructure costs, building an Apache Iceberg‑based data lake, scaling Flink real‑time workloads, and creating a data‑driven growth platform that leverages tracking, A/B testing and analytics.
This article details Jingdong's evolution from Storm to Flink, the architecture of its Kubernetes‑based real‑time computing platform, extensive containerization practices, performance and stability optimizations, and the future plan to unify batch‑stream processing while expanding SQL support and intelligent operations.
This article explains how Flink enables end‑to‑end machine‑learning workflows through AI Flow, covering the background of Lambda architecture, AI task stages, the advantages of Flink, AI Flow components, AI Graph concepts, integration with Python and TensorFlow, and a real‑world advertising recommendation use case.
This article explains how Apache Flink integrates with data lake architectures, especially using Apache Iceberg as a table format, to enable real‑time streaming ingestion, CDC processing, near‑real‑time lambda architectures, and future enhancements like automatic file merging and row‑level deletes.
This article explains how Flink enables end‑to‑end AI workflows through the AI Flow platform, covering the Lambda architecture background, AI task pipeline stages, the reasons for choosing Flink, AI Flow’s graph model, core services, integration with ML pipelines, and real‑world advertising recommendation use cases.
Alibaba’s Customer Experience (CCO) team transformed its real‑time data platform by evolving from a Lambda‑style database architecture to a cloud‑native real‑time data warehouse powered by Hologres and Flink, achieving higher throughput, lower latency, reduced costs, and self‑service analytics for massive Double‑11 traffic.
This article explains Change Data Capture (CDC) and how Flink CDC can be used for incremental data synchronization, real‑time materialized views, audit logging, and CDC‑based joins, providing code examples, Maven dependencies, and SQL/Java snippets for MySQL and Kafka integrations.
Alibaba’s 2020 Double‑11 achieved unprecedented real‑time processing of 4 billion records per second and 7 TB of data per second using Flink, showcasing the stability, performance and efficiency of its stream‑batch unified architecture across diverse business scenarios.
This article introduces Alink, Alibaba's open‑source machine‑learning platform built on Flink, explains its core algorithms, performance comparison with Spark ML, version‑wise feature evolution, and provides practical quick‑start instructions for both Java (Maven) and Python (PyAlink) users, including data source handling, type conversion components, unified file‑system operations, and an overview of its FM algorithm implementation.
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.
This article explains the Flink operator chaining mechanism, detailing how logical plans are transformed into JobGraph and ExecutionGraph, the conditions for chaining, code implementations, and how the runtime constructs OperatorChain to improve execution efficiency.
This article explains the difference between APIs and Java SPI, describes how SPI enables plug‑in development through ServiceLoader, and illustrates its practical use with JDBC driver loading and Flink table factories, providing code snippets and implementation steps for backend developers.
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 article introduces Alink, Alibaba's open‑source machine‑learning platform built on Flink, detailing its core algorithms, performance advantages over Spark ML, version evolution, Maven and PyAlink installation steps, data‑source integrations, FM algorithm support, and unified file‑system operations for both batch and streaming workloads.
The article describes a real‑time video recommendation indexing system that replaces 30‑minute batch builds with an Elasticsearch‑based service, integrates prior and posterior data pipelines, ensures consistency via locking and version checks, enables zero‑downtime upgrades, smooths write spikes, and boosts recall performance through multi‑level caching and ES tuning, delivering sub‑40 ms latency and significant business growth.
This article introduces the Actor model, its fundamental rules, and how it underpins Flink and Spark RPC mechanisms, then explains the Akka framework, its actor hierarchy, supervision, lifecycle, and dispatcher, providing a concise foundation for distributed big‑data processing.
Flink SQL, the high‑level SQL interface for Apache Flink, offers language‑independent, dependency‑free, easy‑to‑use stream processing with advanced features such as DDL, UDFs, time semantics, windowing, pattern matching, and built‑in connectors, supporting data synchronization, batch‑stream fusion, Hive integration, and various product enhancements.
Gaode’s TestPG platform solves full‑link load‑testing bottlenecks by unifying traffic capture with Iflow, converting logs into standardized corpora via a Flink pipeline, and applying corpus‑intelligence that extracts seasonal feature statistics and predicts distributions for precise, feature‑level throttling, enabling faster, more reliable testing and future autonomous optimization.
This guide explains how to set up Apache Zeppelin as an interactive notebook for Flink SQL, covering download, environment configuration, Zeppelin and Flink interpreter settings on YARN, Hive integration, and step‑by‑step testing of streaming SQL queries.
The article explains how Flink 1.11 deepens its integration with Hive, covering background, new connector features, simplified dependency management, enhanced Hive dialect, streaming writes and reads, temporal table joins, and how these capabilities enable a unified batch‑streaming data warehouse.
This article presents NetEase Cloud Music's real‑time computing platform built on Flink and Kafka, covering background, architectural design, Kafka and Flink selection reasons, platformization, warehouse usage, encountered challenges, and the solutions implemented to improve reliability and performance.
This article details Didi’s end‑to‑end construction of a real‑time data warehouse for the Ride‑Sharing (顺风车) business, covering motivations, layer‑by‑layer architecture, naming conventions, StreamSQL capabilities, operational tooling, achieved results, challenges, and future batch‑stream integration plans.
Lyft transformed its legacy data pipeline by designing a cloud‑native, Flink‑based near real‑time analytics platform that ingests billions of events, writes Parquet files to S3, leverages Presto for interactive queries, and implements multi‑stage non‑blocking ETL, fault‑tolerant back‑fill, and extensive performance optimizations.
This article provides a comprehensive overview of modern real‑time big‑data solutions, detailing Spark Structured Streaming capabilities, CarbonData’s storage architecture, Meituan’s Flink deployments, and Huawei Cloud Stream’s unified streaming service, highlighting their features, challenges, and future directions.
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 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 why real‑time data warehouses are needed, outlines their core principles, compares them with offline warehouses, describes typical use cases such as real‑time OLAP, dashboards, feature generation and monitoring, and provides a step‑by‑step guide to designing, implementing, and operating a Flink‑based streaming warehouse with Kafka, HBase, and metadata management.
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.
This article examines the pain points of traditional data warehouse platforms, explains the core concepts and advantages of the Iceberg data lake table format, compares it with Metastore, reviews the current Iceberg community ecosystem, and details NetEase’s practical integration with Hive, Impala, and Flink to improve ETL efficiency and support unified batch‑stream processing.
This article describes ByteDance's single‑task recovery solution for Flink's real‑time computation, detailing the problem of global job restarts, the proposed network‑layer enhancements, upstream and downstream optimizations, JobManager restart strategy, implementation challenges, and the measurable latency and availability benefits achieved in production.
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.
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.
This article explains how Flink's Timer mechanism works, covering its usage in KeyedProcessFunction, the underlying TimerService and InternalTimerService implementations, the role of triggers, and the detailed code paths for processing‑time and event‑time timers, while highlighting performance considerations.
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 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 examines the challenges of existing stream‑processing benchmarks, introduces the open‑source Nexmark framework designed for reproducible, comprehensive performance testing, describes its metrics, query set, workload configurability, and presents experimental results on Flink, highlighting its role in advancing big‑data stream benchmarking.
Facing a surge to over 100 million members, iQIYI rebuilt its monitoring stack by ingesting four log types, adopting Spark Streaming, Flink and Druid for real‑time analysis, and optimizing resource usage, which cut incident resolution time by more than 80 % while supporting billion‑level data volumes.
This article explains how the ADC (Alibaba DChain Data Converger) project automatically creates large real‑time tables by letting users configure metrics on the front‑end, then generating and publishing SQL through a pipeline that leverages design patterns, priority queues, and tree‑based data structures for efficient cross‑database processing.
This article explains why Flink is chosen for online sample generation, describes the end‑to‑end implementation steps—including stream union, state‑timer processing, and output formatting—covers state backend choices, monitoring, validation, fault handling, and platformization for scalable real‑time machine‑learning pipelines.
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 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.
OPPO's data platform engineer Zhang Jun shares the design and implementation of OPPO's real‑time data warehouse built on Apache Flink, covering background, top‑level architecture, practical deployment, and future directions such as enhanced SQL development, resource scheduling, and automated configuration.
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.
To support Didi’s fast‑growing car‑pool service, a real‑time data warehouse was built using a streamlined layered architecture—ODS, DWD, DIM, DWM, and APP—leveraging Flink‑based StreamSQL, Kafka, Druid and ClickHouse to deliver minute‑level analytics, dashboards, monitoring, and cross‑business interfaces while planning unified meta‑store integration.
This article details the design and implementation of a comprehensive crash feedback platform for mobile applications, covering the motivation behind replacing third‑party services, the system architecture using Flink, Kafka and HBase, crash interception on Android, automated grouping and assignment, version filtering, daily reporting, and future enhancements.
DiDi’s Data Channel Service evolved from a fragmented component system into a unified, SLA‑driven platform with a UI‑based Sync Center and Flink‑powered StreamSQL engine, dramatically improving task creation speed, resource utilization, and reliability while automating issue diagnosis for company‑wide real‑time and offline data synchronization.
This article demonstrates how to use Flink 1.11's enhanced Hive integration to stream data from a Kafka source, write it into partitioned Hive tables with checkpoint‑driven commits, and read Hive tables as a continuous source using dynamic table options and table hints.
This article introduces Complex Event Processing (CEP), explains its core concepts and features, details Flink's Pattern API with individual, combined, and group patterns, and provides a complete Scala example that detects three consecutive login failures within three seconds using Flink CEP.
This article presents a comprehensive guide for transferring massive MySQL datasets to HBase, covering environment setup on Ubuntu, three synchronization methods—MySQL LOAD DATA, a Kafka‑Thrift pipeline using Maxwell, and real‑time Flink processing—along with performance comparisons and practical tips for Hadoop, HBase, Kafka, Zookeeper, Phoenix, and related tools.
This comprehensive guide details three practical methods for syncing massive MySQL datasets to HBase—including Sqoop, Kafka‑Thrift, and Flink pipelines—covering environment setup, configuration, code examples, performance comparisons, and optimization tips for large‑scale data ingestion and querying.
This article demonstrates how to implement real-time hot item ranking, page view counting, and unique visitor estimation using Apache Flink with Kafka sources, sliding windows, custom aggregation functions, and a Bloom filter backed by Redis, providing complete Scala code examples.
This article explains the evolution of Apache Flink's SQL support, detailing the Blink Planner architecture, the end‑to‑end Flink SQL workflow, logical and physical planning, code generation, stream‑specific optimizations such as retraction and mini‑batch, and future development directions.
This guide walks through installing InfluxDB and Grafana on CentOS, configuring InfluxDB for Flink metrics storage, creating databases and retention policies, integrating the Flink InfluxDB reporter, and building Grafana dashboards to visualize real‑time Flink job metrics.
This article provides a comprehensive guide to configuring Apache Flink—including job manager and task manager settings, high‑availability via Zookeeper, metrics reporting, as well as Kafka producer tuning and Yarn resource adjustments—to help practitioners optimize big‑data streaming jobs.
This article presents an in‑depth overview of Weibo's large‑scale machine learning platform, detailing its multi‑layer architecture, development workflow, CTR model evolution, and how Apache Flink is employed for real‑time data processing, sample services, multi‑stream joins, multimedia feature generation, and future roadmap plans.
This article shares practical experiences and insights on using different types of state in Apache Flink for real‑time stream processing, covering managed versus raw state, code examples in Scala and Java, handling late data, dimension table joins, distinct semantics, and best‑practice recommendations.
This article presents a comprehensive guide to constructing real-time data warehouses using Apache Flink, covering the motivations, design principles, application scenarios, layer-by-layer architecture, metadata and lineage management, quality assurance, and the supporting toolchain for reliable streaming analytics.
This article details Likee's short‑video recommendation pipeline, covering the evolution of its feature‑engineering framework, the use of Flink for minute‑level statistical and second‑level session features, the integration of automatic statistical features into DNN models, multimodal feature extraction, and the cache‑tensor technique that dramatically improves online inference performance.
iQIYI’s senior data engineer shares the evolution of its big‑data services from Hadoop to a Flink‑based real‑time computing platform, detailing architecture, monitoring improvements, StreamingSQL capabilities, business use cases like recommendation and deep‑learning data generation, and future plans for unified stream‑batch processing.
This comprehensive guide walks you through setting up a pseudo‑distributed Hadoop environment, loading massive MySQL data with LOAD DATA, Python scripts, and multithreading, and then synchronizing the data to HBase using three approaches—Sqoop, a Kafka‑Thrift pipeline, and a real‑time Kafka‑Flink pipeline—while also comparing query performance of HBase and Phoenix.
This article explains how Flink uses task slots to partition TaskManager resources, the benefits of slot sharing, the interaction between Scheduler, SlotPool, and ResourceManager, and the internal classes such as LogicalSlot, PhysicalSlot, and SlotSharingManager that enable resource isolation and sharing in stream processing jobs.
This article explains Flink's checkpoint mechanism, outlines key performance metrics, discusses interval configuration, external state storage choices, resource allocation, and task-local recovery strategies to improve checkpoint speed and reliability in large‑scale state scenarios.
This article details NetEase's evolution from Storm to Flink for real‑time computing, describing the Sloth platform's architecture, large‑scale deployment, diverse business scenarios, monitoring, alerting, and future development plans, illustrating how Flink powers data synchronization, real‑time warehousing, and e‑commerce analytics and recommendation.
Apache Flink 1.11.0 introduces a suite of major enhancements—including unaligned checkpoints, a unified source interface, CDC support in Table API/SQL, performance‑boosted PyFlink, a new application deployment mode, and numerous UI, Docker, and catalog improvements—aimed at increasing usability, scalability, and integration across streaming and batch workloads.
This article explains how FunTouTiao builds a high‑performance real‑time analytics pipeline using Flink, Hive, and ClickHouse, covering business scenarios, hour‑level and second‑level Flink‑to‑Hive architectures, streaming file sink mechanics, multi‑user permissions, ClickHouse performance tricks, and future roadmap for unified stream‑batch storage.
The article outlines a three‑dimensional framework—technical depth, ecosystem breadth, and evolution capability—to help engineers decide which big‑data or stream‑processing technology (such as Hadoop, Spark, or Flink) is worth investing time in, and provides practical tips like using Google Trends and GitHub awesome lists.
This article details Kuaishou's Flink‑based real‑time computing architecture, its massive cluster scale, and the comprehensive strategies—including overload protection, system stability, pressure testing, and resource guarantees—implemented to ensure reliable streaming for the 2020 Spring Festival Gala and its real‑time dashboard.
This article details Shopee Singapore Data Team’s implementation of a Flink‑based real‑time data warehouse, covering background challenges, layered architecture integrating Kafka, HBase, Druid, Hive, streaming pipelines, job management, monitoring, and future plans to expand Flink SQL support.
This article summarizes the objectives, design principles, application scenarios, layer‑by‑layer construction methods, quality assurance mechanisms, and supporting tools for building a real‑time data warehouse using Apache Flink, providing practical guidance for data engineers and architects.
This article compares Apache Flink and Apache Spark in both standalone and YARN deployment modes, detailing their architecture, job scheduling differences, and specific configurations such as Flink’s yarn‑cluster and yarn‑session modes versus Spark’s yarn‑client and yarn‑cluster modes.
QuTouTiao leverages Flink and ClickHouse to build a high‑performance real‑time analytics platform that supports hourly Hive pipelines and sub‑second ClickHouse queries, achieving sub‑second response for 80% of requests through streaming ingestion, exactly‑once semantics, multi‑cluster coordination, and optimized ClickHouse storage and connector designs.
This article analyzes the challenges of traditional data lake and warehouse architectures, explains why unified storage and compute are needed for real‑time and batch workloads, and introduces Hologres as a cloud‑native, high‑performance engine that combines PostgreSQL compatibility with Flink‑driven analytics to deliver a true real‑time data warehouse solution.
This article describes how Kuaishou leverages Apache Flink for large‑scale real‑time multi‑dimensional analytics, details the architecture of its analytics platform using Kudu storage and KwaiBI, and introduces SlimBase—a lightweight, embedded shared state backend that replaces RocksDB to reduce I/O, latency, and CPU overhead.
