This article explains how to compute the top‑N hot products or brands within a time window using Apache Flink, covering data modeling, event‑time handling, sliding windows, custom aggregation functions, and result sorting with complete Java code examples.
This article describes the evolution of a real‑time computing platform from SQL 1.0 built on Spark Structured Streaming to SQL 2.0 powered by Flink‑SQL, covering dynamic tables, continuous queries, dimension‑table joins, cache optimization, DDL extensions, platformization, operational challenges and future roadmap.
This article presents Kuaishou's extensive use of Apache Flink for real-time multi-dimensional analytics, detailing the platform's architecture, cluster scale, data processing pipelines, the design of a shared state storage engine called SlimBase, and performance improvements achieved through replacing RocksDB with a customized HBase‑based solution.
This article explains the three time notions supported by Apache Flink—ProcessTime, EventTime, and IngestionTime—detailing their semantics, how Watermarks enable event‑time processing, and provides Scala code samples for configuring time characteristics, assigning timestamps, and generating Watermarks in a streaming job.
This article details the evolution of 58 Tongcheng Bao's real‑time data warehouse, describing the initial Spark‑Streaming architecture, its limitations, and the redesign using Flink with a layered ODS‑DWD‑DWS‑APP model, data‑quality monitoring, join techniques, and the resulting improvements in latency and accuracy.
This article explains Flink's StreamingFileSink in version 1.10.0, covering how files transition through In‑progress, Pending, and Finished states, the bucket assignment and rolling policies, and provides a complete Java example for writing string data to files.
Facing growing order volumes and strict timeliness demands, Cainiao’s tech team overhauled its real‑time data warehouse by redesigning data models, adopting Flink for streaming computation, upgrading data services, and exploring innovative tools, sharing practical lessons and future directions for large‑scale logistics analytics.
This article examines Apache Flink’s programming model, comparing its batch DataSet API with the streaming DataStream API, detailing class hierarchies, key code examples such as groupBy and job submission, and explaining how both paradigms are unified into a common JobGraph representation.
This article explains how to combine the Flink data‑processing engine with TensorFlow to create a unified, end‑to‑end machine‑learning workflow, covering background, challenges, the Flink‑AI‑extended architecture, ML framework and operator abstractions, and both batch and streaming training and prediction modes.
This article explains Flink's role in HuoLaLa's risk‑control system, covering its background, the Lambda‑style architecture that combines batch and streaming, the real‑time data pipeline, machine‑learning models, and operational safeguards that together enable proactive fraud detection.
This article shares Cainiao's practical experience in constructing a real-time data warehouse, covering the shortcomings of the previous architecture, the evolution of data models, the migration to Flink with advanced features like retraction and timer services, and the modernization of data services and tooling to support high‑throughput logistics scenarios.
This article explains the new unified TaskManager memory model introduced in Flink 1.10, detailing each memory component, its configuration parameters, how they map to JVM settings, and practical guidance for both standalone and containerized deployments, including a concrete YARN example.
This article outlines a comprehensive real‑time data system built on Apache Flink, covering its application scenarios, layered architecture, data model stratification, construction methods, and a concrete Flink SQL example for calculating UV metrics from Kafka‑sourced page‑view data.
This article explains Apache Flink's ProcessFunction and CoProcessFunction, detailing their use of events, state, and timers, compares event‑time and processing‑time semantics, and provides a complete Java example illustrating timer registration, onTimer handling, and debugging observations.
The article outlines a three‑dimensional framework—technical depth, ecosystem breadth, and evolution capability—to help professionals evaluate whether a technology is worth investing time in, illustrated with real‑world examples from Hadoop, Spark, and Flink.
This article reviews the evolution of data warehouses from traditional offline models to modern real‑time architectures, presenting detailed case studies of Meituan, NetEase, Zhihu, and OPPO, and discusses layer designs, technology choices such as Flink, Kafka, and storage options, and key lessons for building scalable real‑time warehouses.
This article details Dada Group's development of the Dada Flink SQL engine, describing its background, architecture, parser design, dimension‑table join strategies, numerous enhancements such as HA support, Kafka keyword handling, metadata integration, Redis and ClickHouse sinks, BINLOG simplification, and future migration plans toward Flink 1.10.
This article explains how to read and compare the RPC implementations of Spark and Flink, covering Actor Model concepts, Akka integration, message handling, threading models, and practical code‑reading techniques while providing detailed code excerpts and architectural analysis.
This article enumerates typical Apache Flink deployment, job, and checkpoint errors—such as JDK version issues, resource shortages, task manager timeouts, and state migration problems—and provides practical troubleshooting steps and configuration tips to help engineers quickly diagnose and fix these failures.
This article explains how Apache Flink's flexible state management can be leveraged to solve data correlation challenges in real‑time recommendation platforms, compares Flink with Spark and Storm, describes the underlying broadcast and managed state mechanisms, and provides a step‑by‑step implementation using Kafka, Druid, and custom broadcast functions.
This article examines the challenges of maintaining separate offline and real‑time data warehouses, explains the three‑layer ODS‑DW‑ADS model, evaluates the traditional Lambda architecture, and explores how a unified Flink stack with Kafka, HiveCatalog and streaming sinks can simplify metadata, SQL development, data import/export, and stateful processing for both batch and streaming workloads.
This article presents a comprehensive guide for synchronizing massive MySQL datasets to HBase, covering environment preparation, fast MySQL data loading techniques, and three practical pipelines—Sqoop, Kafka‑Thrift, and Kafka‑Flink—along with performance comparisons and optimization tips for large‑scale data processing.
This article explains how to replace Flink's default Log4j logger with Logback by adding Maven dependencies, excluding transitive Log4j artifacts, updating Flink's lib directory, and customizing Logback XML configurations including a rolling file appender and optional email alerts.
This article describes how iQIYI built a real‑time, multi‑dimensional log monitoring platform using Spark Streaming, Flink, Kafka and Druid to handle billions of logs, improve alerting accuracy, reduce incident response time, and outline future intelligent monitoring enhancements.
To support over 100 million iQIYI members, the team rebuilt a real‑time log monitoring platform that gathers access, exception, Nginx and front‑end logs via a Venus‑Agent, streams them through Kafka to Spark Streaming and Flink, stores metrics in Druid, and provides minute‑level host and business alerts, achieving 80 % faster incident investigation, detecting 90 % of member complaints early, and generating more than 4,800 actionable alerts.
This article explains how Flink's flexible state management, including Broadcast, Keyed, and Operator states, can be used to solve real‑time recommendation challenges such as per‑minute UV, click, and exposure counting, while addressing locality mapping and data‑delay issues with Druid as the downstream store.
This article explains how Apache Flink implements fault‑tolerant checkpointing using the Asynchronous Barrier Snapshot (ABS) algorithm, a localized version of the Chandy‑Lamport distributed snapshot, covering barriers, snapshot alignment, exactly‑once versus at‑least‑once semantics, and handling of cyclic dataflow graphs.
This article outlines Ctrip's flight ticket data warehouse evolution, current big‑data technology stack, data synchronization methods, layered architecture, quality monitoring system, and a real‑time price anomaly detection case, providing practical insights for building scalable, reliable data warehousing solutions.
This article presents the design of Flink's batch processing architecture, its integration with Hive through a unified Catalog API, details the enhancements in Flink 1.10, outlines future work, and reports a performance test showing roughly seven‑fold speedup over Hive on MapReduce.
This article examines how Apache Flink embraces AI by leveraging the Lambda architecture and stream‑batch unification to enable real‑time data processing across preprocessing, model training, and inference, discusses the challenges of model updates and code maintenance, and outlines ongoing Flink initiatives that support AI real‑timeization.
The article details iQIYI’s RAP platform, describing its real‑time analytics requirements, architectural evolution from RAP 1.x to 2.x, core design steps, integration of Druid, Spark, Flink, and KIS, and showcases business use cases such as membership monitoring, recommendation evaluation, and smart‑TV alerting.
This article explains how Apache Flink uses event‑time windows, watermarks, and allowed lateness to handle out‑of‑order and late data, and provides complete Scala code examples that demonstrate timestamp assignment, watermark generation, window triggering, and side‑output of late records.
This article explains how to use Flink's Broadcast State to track user purchase paths in real time, detailing the design, required Kafka streams, Java APIs, state management, dynamic configuration, code implementation, deployment steps, and example results for a big‑data streaming application.
This article explains how Apache Flink uses event‑time timestamps and watermarks to handle out‑of‑order and late data, describes the assignTimestampsAndWatermarks API with periodic and punctuated watermark assigners, and provides practical code examples for window lateness and side‑output handling.
The talk details Cainiao’s evolution of its real‑time data warehouse architecture, covering the original 2016 model, compute and service challenges, the 2017 multi‑layer data model redesign, migration to Flink, practical cases of state retraction, timeout statistics, smart optimizations, and the unified data service platform.
The article describes how Xianyu’s recommendation system shifts complex event processing from server‑side Blink to client‑side Python using Flink CEP concepts, detailing the NFA‑based state and transition model, pattern‑building API, aggregation support, achieving sub‑second execution with modest memory, and outlines future optimizations such as NFA persistence, windowing, DSL script generation, and C++/TensorFlow Lite acceleration.
The article describes Ctrip's Prophet platform, which combines Flink real‑time stream processing with TensorFlow deep‑learning models to provide intelligent, low‑latency anomaly detection, replacing traditional rule‑based alerts and addressing challenges such as holiday traffic and model scalability.
Meituan-Dianping’s senior technical expert shares the evolution, architecture, and implementation of their Apache Flink‑based real‑time data warehouse platform, covering platform evolution, layered design, job and resource management, business warehouse use cases, and future development considerations.
The article describes how to troubleshoot and fix Oozie shell‑action failures when submitting Flink jobs on a CDH 6.3 cluster with Kerberos, detailing environment‑variable conflicts, error messages, and the final solution using a clean environment and custom FLINK_CONF_DIR settings.
The article details Xiaomi's unified streaming data platform, its three‑generation evolution from Scribe/Kafka/Storm to Talos and Flink, the current architecture supporting billions of records daily, and future plans to unify offline and real‑time warehousing with Flink SQL.
This article explains how Alibaba 1688 achieves real‑time recommendation, advertising, and product statistics through a robust middle‑platform foundation, streaming engines like Blink, data synchronization tools, and scalable storage, illustrating three concrete engineering cases and the end‑to‑end real‑time data service pipeline.
This article details how Alibaba migrated its massive Taobao‑Tmall search workload to the search offline platform, tackling challenges of massive data volume, one‑to‑many joins, and hotspot sellers through a series of performance optimizations—including local joins, salt‑based data sharding, dynamic aggregation jobs, and asynchronous processing—to achieve high‑throughput full loads and low‑latency incremental updates.
The article presents a comprehensive overview of Xiaomi's streaming platform, detailing its three‑stage evolution from a Scribe‑Kafka‑Storm stack to a Flink‑driven real‑time data warehouse, describing its architecture, components, challenges, migration strategies, job and SQL management, and future roadmap.
This article presents a comprehensive real‑time and offline integrated solution for a channel analysis system, detailing challenges, architecture, implementation using Flink, Spark Streaming, Kafka, Elasticsearch, and HIVE, and demonstrating minute‑level latency and high accuracy through performance evaluations.
This article details{32-64 words} OPPO's evolution from an offline data warehouse to a real‑time platform, describing the business scale, data‑mid platform architecture, migration strategy using Flink SQL, extensions like AthenaX, and practical use cases such as real‑time ETL, CTR calculation, and tag import.
This article compiles 130 interview questions spanning big data technologies, distributed systems, and core computer science concepts to help candidates prepare for technical interviews, offering a comprehensive resource for self‑study and review.
The article details iQIYI's Real-Time Analysis Platform (RAP), describing its motivation, architecture evolution from RAP 1.x to 2.x, OLAP engine selection, product design workflow, integration of Druid KIS and Flink, enhanced diagnostics, and real-world applications in membership monitoring, recommendation evaluation, and smart TV alerting.
This article explains how to handle dimension table joins in Apache Flink streaming by leveraging Async I/O to perform non‑blocking external lookups, provides detailed code examples for both synchronous and asynchronous functions, discusses configuration parameters, and outlines best practices and pitfalls.
iQIYI’s Real‑Time Analysis Platform (RAP) combines Apache Druid with Spark/Flink to deliver minute‑level, low‑latency multidimensional analytics via a web wizard, supporting hundreds of streaming tasks and thousands of reports across membership, recommendation, and TV monitoring, while simplifying development and maintenance.
This article examines the design of a real-time data warehouse built on Flink, Kafka, and HBase, compares it with traditional offline warehouses, and discusses key challenges such as data accuracy, latency, and the complexity of maintaining real-time dimension tables.
The article describes the evolution from the legacy XDATA tool to the new XFlink system, detailing its architecture, core plugins, parser and deployment modules, resource management with Yarn, monitoring via Prometheus and Grafana, and planned enhancements such as Flink SQL configuration and modular plugins.
The article details how 58.com designed and evolved its one‑stop real‑time computation platform Wstream, migrating from Storm and Spark Streaming to Apache Flink, and describes the architecture, task isolation, stream‑SQL features, monitoring, and ongoing optimizations that enable processing of over 600 billion records daily.
Flink’s StreamPartitioner abstracts data routing in DataStream, offering eight built‑in partitioners—including Global, Shuffle, Rebalance, KeyGroup, Broadcast, Rescale, Forward, and Custom—each with distinct channel selection logic, illustrated with source code snippets and explanations of their runtime behavior.
This article explains the architecture of a Flink cluster, detailing the startup procedures for JobManager and TaskManager, the three deployment modes, and the end‑to‑end flow of a Flink job from client code through StreamGraph, JobGraph, ExecutionGraph to the physical execution on TaskManagers.
This article details Alibaba's massive Flink deployment, covering its historical background, the operational challenges of managing tens of thousands of nodes, the design of a comprehensive Flink management platform, and the automated solutions for fault handling, resource allocation, and performance testing in a large‑scale big‑data environment.
This article explains Flink's sliding window mechanism, shows how the WindowAssigner and WindowOperator work with code examples, analyzes the performance impact of fine‑grained sliding windows, and proposes a practical workaround using tumbling windows combined with external storage such as Redis for efficient PV/UV aggregation.
This article lists and briefly describes the ten most starred Java open‑source projects on GitHub for November, covering tools such as NLP libraries, learning guides, big‑data frameworks, rapid‑development platforms, algorithm collections, job schedulers, code‑style checkers, traffic‑control systems, the Spring framework, and service‑discovery solutions.
This article explains how to develop a real‑time TopN ranking feature using Apache Flink, covering both global and grouped TopN implementations, nested TopN strategies, and provides complete Java code snippets for environment setup, word counting, windowing, and custom TopN functions.
Alink, Alibaba’s open‑source machine‑learning platform built on Apache Flink, offers a rich library of batch and streaming algorithms, a Python API, iterative computation optimizations, and real‑world case studies, positioning it as a powerful AI solution for large‑scale, low‑latency data processing.
This article provides an in‑depth Flink interview guide covering the framework’s core concepts, advanced features such as fault‑tolerance, state management, and checkpointing, as well as detailed explanations of its architecture, APIs, partitioning strategies, and source‑code flow, complete with code examples.
This article explains how to create custom Flink SourceFunction and SinkFunction implementations, demonstrates a RocketMQ source and an HBase sink with full code examples, and discusses checkpointing, event‑time handling, and deployment of the streaming job on a Flink‑on‑YARN cluster.
This article explains how to dynamically update configuration data in real‑time Spark Streaming and Flink jobs using broadcast variables and broadcast state, providing Java code examples and discussing the limitations and practical considerations of each approach.
This article explains the background, source‑code analysis, and practical implementation of Flink's LatencyMarker feature for measuring end‑to‑end job latency, including metric exposure, configuration options, and code snippets illustrating how latency markers are emitted and processed within the streaming pipeline.
This article explains how Flink manages memory through its MemorySegment abstraction, the implementations of HeapMemorySegment and HybridMemorySegment, the role of ByteBuffer, NetworkBufferPool and LocalBufferPool, and details the end‑to‑end data flow from RecordWriter to Netty transport, including key code snippets.
The article summarizes Guo Yi’s 2019 Alibaba Cloud conference talk, outlining Xiaohongshu’s personalized recommendation architecture, detailing the data stack from ingestion to warehouse, and showcasing a Flink‑based real‑time multi‑dimensional user behavior aggregation use case, followed by a vision for the next year’s data architecture evolution.
This article provides a comprehensive overview of Flink SQL window functions, detailing time‑based window types, their underlying implementation in the StreamExecGroupWindowAggregate operator, the processing flow of WindowOperator, timer handling, emit/trigger strategies, and practical code examples for Tumble, Hop, and Session windows.
The article demonstrates how to enrich Flink streaming ETL jobs with slowly changing dimension data by periodically loading MySQL tables into an in‑memory cache and performing a simple map‑side join within a custom RichMapFunction implementation.
The article examines how Apache Flink's state management features, including QueryableState and the upcoming Savepoint Processor API, can serve as a lightweight database for real‑time applications, discussing their advantages, limitations, and practical usage scenarios.
The article details the technical evolution of 58.com’s real-time computing platform—from Storm and Spark Streaming to a Flink‑based one‑stop solution called Wstream—covering use cases, architecture, stability measures, migration from Storm, operational diagnostics, and future development plans.
This article presents a detailed overview of G7's AI‑plus‑Big‑Data‑plus‑IoT platform for logistics, describing its neutral open architecture, real‑time data pipelines using Kafka and Flink, Lambda‑style storage in HBase/Hive, and the resulting safety‑insurance and analytics capabilities.
To accelerate growth on Xianyu’s 20 million‑DAU platform, the team built an event‑driven rule engine with a SQL‑like DSL that translates user‑behavior streams into real‑time Flink/Blink queries, cutting rule development from four days to half a day and achieving sub‑5‑second processing latency.
This article details the technical evolution of 58.com’s real‑time computing platform, describing the shift from Storm and Spark Streaming to Apache Flink, the design of the one‑stop Wstream platform, its large‑scale deployment, stability measures, SQL streaming capabilities, task migration, diagnostics, optimizations, and future plans.
This article provides a thorough introduction to data warehouses, traces their evolution, explains construction methodologies, compares offline, Lambda, and Kappa architectures, and presents real‑time warehouse case studies from Alibaba, Meituan, Xiaomi, Netflix, and OPPO, highlighting practical implementation details and challenges.
This article explains the fundamentals of JVM garbage collection, its generational algorithms and associated performance issues, and then details Apache Flink's memory management architecture, including MemorySegment, off‑heap buffers, serialization mechanisms, and type information for efficient big‑data processing.
Data skew, a common challenge in large-scale data processing where uneven key distribution leads to performance bottlenecks, is explored with examples from Hadoop, Spark, and Flink, alongside practical mitigation techniques such as hotspot key redesign, map‑side joins, and tuning framework parameters.
This article compares Flink and Spark Structured Streaming, detailing their differences in join capabilities, state management, fault‑tolerance mechanisms, exactly‑once semantics, back‑pressure handling, and table registration, while providing code examples and practical insights for real‑time big‑data processing.
This article explains how to use Flink 1.9's built‑in Kafka connector, covering supported versions, Maven dependencies, consumer and producer configuration in Java and Scala, checkpointing, offset handling, partition discovery, timestamps, watermarks, and provides a complete runnable example.
This article examines Xiaomi's migration from Spark Streaming to Apache Flink, comparing scheduling strategies, mini‑batch versus true streaming, resource utilization, latency, and serialization mechanisms, and concludes with practical insights and custom optimization techniques for large‑scale data processing.
This article demonstrates how to build a one‑second‑refresh real‑time dashboard for e‑commerce order data using Apache Flink, Kafka, and Redis, covering JSON message parsing, processing‑time windows, stateful aggregation for site‑level KPIs, and efficient top‑N product ranking via Redis sorted sets.
This article details Beike's real‑time computing engine, covering its background, streaming platform built on Spark Streaming and Flink, data ingestion via Kafka, metadata handling, SQL‑based task development, monitoring, storage solutions, and future roadmap for resource management and AI‑enhanced monitoring.
This article explains how to build a real-time search engine indexing pipeline using Flink, covering background, batch versus incremental indexing strategies, a hybrid architecture that merges both approaches, and a concrete cloud‑based implementation involving MySQL binlog, Logtail, SLS, and Elasticsearch.
This article details how Weibo’s advertising team designed and implemented a real‑time data platform capable of processing over a hundred billion daily logs, covering technology selection, Flink advantages, architecture evolution, data processing pipelines, component libraries, fault‑tolerance strategies, and the construction of a multi‑layer real‑time data warehouse.
This article explains how to design and implement a real‑time data verification framework using Flink to generate wide tables, storing detailed records in Elasticsearch or HDFS with Hive for cross‑checking against offline data, ensuring trustworthy metrics for dashboards and stakeholders.
Xianyu’s Complex Event Processing system, built on Alibaba’s Blink (Flink) and a custom SQL‑like DSL, standardizes event I/O, lets users define sequence, window and aggregation rules, and combines an interactive rule service, SLS source, parser, job manager and MetaQ sink to achieve ~100 k QPS, sub‑second latency, fault‑tolerant, and rule‑to‑production turnaround in about thirty minutes.
The article introduces JDNoSQL, a distributed column‑oriented key‑value store built on HDFS, outlines its core features, describes various business scenarios including real‑time ad computation, details the system architecture with Kafka and Flink, and presents table designs for ad impression and click statistics.
This article demonstrates how to build a real‑time education analytics system by streaming answer data through Kafka into Flink or Spark, performing per‑question, per‑grade, and per‑subject aggregations, and optionally accelerating development with UFlink SQL.
This article describes how 58.com’s commercial engineering team redesigned its real‑time feature‑mining pipeline—replacing a minute‑level Spark Streaming framework with Flink—to achieve sub‑second latency, higher throughput, stronger fault‑tolerance, and end‑to‑end exactly‑once semantics for user‑profile generation in the second‑hand‑car recommendation scenario.
This article explains how Flink checkpoints persist state, compares the three built‑in state backends (MemoryStateBackend, FsStateBackend, RocksDBStateBackend), discusses their configurations, advantages, limitations, and provides guidance on selecting the appropriate backend for different big‑data streaming scenarios.
This article describes how Autohome migrated its real‑time analytics from Storm to a Flink‑SQL platform, detailing the architectural design, development and operational advantages, practical use cases such as recommendation metrics, and future plans for ecosystem expansion and open‑source release.
After Flink 1.9 deprecated the legacy Type API in favor of DataTypes, users encounter missing schema TypeInformation methods, and this article explains the root cause and provides a code solution to convert legacy types using TypeConversions and register a TableSink.
This article explains the fundamentals of the two‑phase commit protocol, details its two stages (prepare and commit), discusses its advantages and drawbacks, and shows how Apache Flink implements 2PC for exactly‑once semantics with Kafka using the TwoPhaseCommitSinkFunction and related code examples.
This tutorial explains how to run Apache Flink jobs on Kubernetes by building Docker images, deploying JobManager and TaskManager components with Kubernetes manifests, configuring high‑availability with ZooKeeper and HDFS, and using SavePoints and scaling techniques to manage and extend Flink streaming applications.
This article explains Flink's checkpoint mechanism, its execution flow, common configuration options, and the benefits and considerations of incremental checkpoints using the RocksDB state backend, providing practical code examples and YAML settings for reliable stream processing.
This guide provides a detailed overview of Apache Flink covering its core streaming engine, APIs (DataSet, DataStream, Table), architectural components, comparison with Spark Streaming, partitioning, parallelism, restart strategies, state backends, time semantics, watermarks, SQL processing, fault‑tolerance mechanisms, memory management, serialization, RPC framework, back‑pressure handling, operator chaining, and practical tips for interview preparation.
This article presents a comprehensive collection of Flink interview questions covering fundamental concepts, advanced topics, and source‑code details to help candidates prepare effectively for Flink‑related technical interviews.
This article details Zhihu's real‑time data warehouse evolution, describing the 1.0 Spark Streaming architecture, its limitations, and the 2.0 redesign that introduces Flink, layered data models, streaming and batch ETL, metric storage choices, and future roadmap for scalable, low‑latency analytics.
This article provides a comprehensive interview roadmap for big data developers, outlining essential Java fundamentals, JVM internals, Linux basics, distributed theory, core frameworks such as Hadoop, Spark, Flink, Kafka, Netty, HBase, Hive, and practical algorithm topics, while also offering resume and career advice for aspiring candidates.
The article details the six‑layer big‑data architecture of an online school, chronicles its migration from Storm to Spark Streaming and finally to Flink, and showcases concrete real‑time applications such as gateway monitoring, user‑profile tagging, renewal reporting, and advertising analysis, while outlining future development directions.
This article explains how Flink CEP, a complex event processing library for Apache Flink, is employed at Haolo Mobility to detect intricate patterns in endless data streams by modeling patterns as states and using pattern conditions for state transitions, illustrating its practical application in real‑world big‑data scenarios.
This article details the design, implementation, and optimization of a Flink‑based real‑time computing platform at Tongcheng‑Elong, covering the evolution from Storm to Flink, support for FlinkSQL and FlinkStream, metric collection, logging, data lineage, savepoint management, and numerous stability fixes contributed back to the open‑source community.
