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980 articles · Page 10 of 10
Big Data Technology & Architecture
Big Data Technology & Architecture
Jun 30, 2019 · Big Data

Curated Collection of Big Data, Flink, Hadoop and Real‑Time Computing Articles from the “Big Data Technology and Architecture” Series

This article presents a carefully organized catalogue of over a hundred technical posts covering Flink source‑code analysis, fundamental and advanced big‑data structures, Hadoop ecosystem components, real‑time streaming with Spark and Kafka, as well as system design guidelines and miscellaneous insights, each linked to its original publication for easy reference.

FlinkHadoopStreaming
0 likes · 6 min read
Curated Collection of Big Data, Flink, Hadoop and Real‑Time Computing Articles from the “Big Data Technology and Architecture” Series
Big Data Technology & Architecture
Big Data Technology & Architecture
Jun 22, 2019 · Backend Development

Understanding Back Pressure in Flink and Its Implementation

The article explains what back pressure is in Flink streaming jobs, why it occurs when data generation outpaces downstream consumption, how Flink monitors it via stack‑trace sampling, configurable parameters, Web UI visualization, and compares the approach with Spark Streaming's back pressure mechanism.

Data pipelinesFlinkSpark
0 likes · 5 min read
Understanding Back Pressure in Flink and Its Implementation
Xianyu Technology
Xianyu Technology
Jun 20, 2019 · Big Data

Design of a High-Performance Real-Time Data Processing System for Service Diagnosis

The paper presents a high‑performance real‑time data processing pipeline that collects, transports, preprocesses, and computes service logs and metrics using Alibaba Logtail, LogHub, and an enhanced Flink (Blink) engine, persisting root‑cause graphs in Lindorm, achieving sub‑3‑second latency for tens of millions of events per second and cutting diagnosis time to about five seconds.

Data ProcessingFlinkarchitecture
0 likes · 10 min read
Design of a High-Performance Real-Time Data Processing System for Service Diagnosis
Big Data Technology & Architecture
Big Data Technology & Architecture
Jun 18, 2019 · Big Data

Understanding Watermarks, Event Time, and Processing Time in Apache Flink

This article explains the three time concepts in Flink—Process Time, Event Time, and Ingestion Time—illustrates their impact on windowed computations with examples, introduces watermarks and allowed lateness for handling out‑of‑order data, and provides complete Scala code for both processing‑time and event‑time streaming applications.

EventTimeFlinkScala
0 likes · 13 min read
Understanding Watermarks, Event Time, and Processing Time in Apache Flink
360 Zhihui Cloud Developer
360 Zhihui Cloud Developer
Jun 4, 2019 · Big Data

Why Flink Outperforms Storm: Deep Dive into Stream Processing Performance

Based on data transmission and reliability metrics, this article compares Apache Storm and Apache Flink in stream processing, presenting benchmark designs, test environments, results for synthetic and Kafka data, and offers practical recommendations such as operator chaining, object reuse, and checkpoint strategies to maximize Flink performance.

FlinkStormStream Processing
0 likes · 13 min read
Why Flink Outperforms Storm: Deep Dive into Stream Processing Performance
360 Tech Engineering
360 Tech Engineering
Jun 3, 2019 · Big Data

Performance Comparison of Apache Storm and Apache Flink from Data Transmission and Reliability Perspectives

This article presents a detailed performance benchmark comparing Apache Storm and Apache Flink in stream processing, focusing on data transmission methods, reliability mechanisms, operator chaining, and both self‑generated and Kafka‑sourced workloads, and provides practical optimization recommendations based on the results.

Data TransmissionFlinkStorm
0 likes · 10 min read
Performance Comparison of Apache Storm and Apache Flink from Data Transmission and Reliability Perspectives
DataFunTalk
DataFunTalk
Jun 3, 2019 · Big Data

Choosing a Real-Time Computing Engine Based on Kafka: Spark vs Flink

This article examines the need for real‑time computation, explains streaming versus real‑time concepts, and compares Apache Spark and Apache Flink—covering their architectures, micro‑batch and continuous processing, advantages, limitations, windowing, event‑time handling, and watermarks—to guide engine selection for Kafka‑driven workloads.

FlinkKafkaSpark
0 likes · 15 min read
Choosing a Real-Time Computing Engine Based on Kafka: Spark vs Flink
Big Data Technology & Architecture
Big Data Technology & Architecture
Jun 2, 2019 · Big Data

Tencent's Oceanus Real-Time Stream Computing Platform and Flink Optimizations

The article presents Tencent's evolution of real‑time stream processing using Flink, the design of the Oceanus one‑stop visual platform, and a series of deep extensions and optimizations—including UI redesign, JobManager failover, checkpoint handling, enhanced windows, LocalKeyBy, idle detection, and log isolation—aimed at supporting petabyte‑scale data workloads.

FlinkOceanusStream Processing
0 likes · 16 min read
Tencent's Oceanus Real-Time Stream Computing Platform and Flink Optimizations
Big Data Technology & Architecture
Big Data Technology & Architecture
May 29, 2019 · Cloud Native

Real-Time Computing Solutions with Flink and HBase: Architecture, Market Analysis, and Use Cases

The article presents Alibaba Cloud's real-time computing solution based on Flink and HBase, covering market competition, open‑source ecosystem, containerized architecture on Kubernetes, and typical applications such as online education video analysis, city‑brain traffic management, and fraud detection.

Cloud NativeFlinkHBase
0 likes · 12 min read
Real-Time Computing Solutions with Flink and HBase: Architecture, Market Analysis, and Use Cases
Big Data Technology & Architecture
Big Data Technology & Architecture
May 28, 2019 · Big Data

Optimizing Flink Shuffle: New Flow‑Control Mechanism, Serialization Improvements, and Architecture Refactoring

The article explains how Flink's shuffle pipeline—from upstream data serialization to downstream consumption—is optimized through a credit‑based flow‑control mechanism, zero‑copy network buffers, broadcast serialization reduction, external shuffle service, and a plugin‑based shuffle manager, resulting in significant performance gains for both streaming and batch jobs.

FlinkFlow ControlShuffle
0 likes · 15 min read
Optimizing Flink Shuffle: New Flow‑Control Mechanism, Serialization Improvements, and Architecture Refactoring
Alibaba Cloud Developer
Alibaba Cloud Developer
May 23, 2019 · Big Data

How Blink Powers Alibaba’s Real‑Time Supply‑Chain Data Warehouse

This article explains how Alibaba's Blink engine tackles the complex challenges of building a real‑time supply‑chain data warehouse—covering retroduction, dimension‑table joins, data skew, timeout statistics, zero‑point optimizations, and future directions—through SQL‑based stream processing and intelligent resource tuning.

Data SkewDimension joinFlink
0 likes · 14 min read
How Blink Powers Alibaba’s Real‑Time Supply‑Chain Data Warehouse
Youzan Coder
Youzan Coder
Apr 29, 2019 · Big Data

Optimizing Flink Sliding Windows for Super Long Time Ranges

To overcome severe performance degradation of Flink sliding windows over very long time ranges, Youzan engineers applied time‑slicing based on the greatest common divisor of window length and slide step, reducing state writes and timers, which yielded 3‑8× speedups in production.

FlinkRocksDBWindow Functions
0 likes · 18 min read
Optimizing Flink Sliding Windows for Super Long Time Ranges
JD Retail Technology
JD Retail Technology
Apr 18, 2019 · Big Data

Data Heterogeneity with BinLake, Binlog, and Flink: Approaches for Order, Subscription, and Product Data

The article explains how data heterogeneity is achieved using JD's BinLake to capture MySQL binlogs, with Flink handling sequential and parallel consumption for order, subscription, and product data, discussing challenges such as ordering guarantees, idempotency, IO overhead, and the shift toward stream‑processing architectures.

ElasticsearchFlinkStream Processing
0 likes · 5 min read
Data Heterogeneity with BinLake, Binlog, and Flink: Approaches for Order, Subscription, and Product Data
ITPUB
ITPUB
Apr 4, 2019 · Big Data

Achieving Sub‑Second Real‑Time Product Selection with Xianyu’s Mach and Blink

Xianyu’s Mach system tackles the e‑commerce challenge of instantly selecting high‑quality items from billions of products by leveraging Blink’s low‑latency stream computing, detailing its architecture—including state, windows, custom UDX functions, data merging, rule execution, and SQL‑to‑MVEL conversion—to achieve sub‑second processing at massive scale.

FlinkRule EngineStream Processing
0 likes · 18 min read
Achieving Sub‑Second Real‑Time Product Selection with Xianyu’s Mach and Blink
dbaplus Community
dbaplus Community
Mar 21, 2019 · Big Data

How Real-Time Data Platforms Evolve: From Storm to Flink and Kubernetes

This article summarizes Wang Xinchun's 2018 DAMS China Data Asset Management Summit talk, detailing the current state, core services, responsibilities, evolution, architecture, challenges, and future directions of a large‑scale real‑time data platform built on Storm, Spark, Flink, and Kubernetes, including a unified data management approach.

FlinkKubernetesSpark
0 likes · 22 min read
How Real-Time Data Platforms Evolve: From Storm to Flink and Kubernetes
Big Data Technology & Architecture
Big Data Technology & Architecture
Mar 21, 2019 · Big Data

Apache Flink Table API Tutorial and End‑to‑End Examples

This article provides a comprehensive tutorial on Apache Flink's Table API, explaining its concepts, core features, and a wide range of operators such as SELECT, WHERE, GROUP BY, UNION, JOIN, and various window functions, while offering complete Scala code examples, custom sources, sinks, and an end‑to‑end job that computes page‑view counts per region using event‑time tumbling windows.

FlinkSQLScala
0 likes · 36 min read
Apache Flink Table API Tutorial and End‑to‑End Examples
Youzan Coder
Youzan Coder
Mar 20, 2019 · Big Data

Evolution of Real-Time Computing at Youzan: From Storm to Flink and Future Directions

Youzan’s real‑time computing platform progressed from early Storm deployments through Spark Streaming to a Flink‑based architecture, adding unified task management, monitoring, and dedicated streaming clusters, while now pursuing SQL‑driven jobs, a Druid OLAP engine, and a future real‑time data warehouse.

FlinkSpark StreamingStream Processing
0 likes · 14 min read
Evolution of Real-Time Computing at Youzan: From Storm to Flink and Future Directions
DataFunTalk
DataFunTalk
Mar 7, 2019 · Big Data

Design and Evolution of Didi's Real‑Time Data Computing Platform

The article details how Didi built and iterated its real‑time data platform, describing the shift from MySQL‑based batch processing to a Kafka‑Samza‑Druid architecture with Spark Streaming and Flink, the challenges addressed, and the current capabilities and operational metrics.

DruidFlinkKafka
0 likes · 9 min read
Design and Evolution of Didi's Real‑Time Data Computing Platform
dbaplus Community
dbaplus Community
Feb 28, 2019 · Big Data

How Zhihu Built a Real-Time Data Warehouse: From Spark Streaming to Flink

This article details Zhihu's evolution of its real-time data warehouse, covering the 1.0 version built on Spark Streaming, the 2.0 upgrade using Flink Streaming SQL, architectural layers, ETL processes, and future directions such as streaming SQL platformization and automated result validation.

ETLFlinkLambda Architecture
0 likes · 19 min read
How Zhihu Built a Real-Time Data Warehouse: From Spark Streaming to Flink
Big Data Technology & Architecture
Big Data Technology & Architecture
Feb 26, 2019 · Big Data

Deploying Apache Flink Clusters: Standalone and YARN Modes

This guide explains how to set up an Apache Flink cluster on CentOS 7 using three deployment methods—Local, Standalone, and Flink on YARN/Kubernetes—including host configuration, SSH setup, package distribution, configuration file editing, cluster start/stop commands, YARN resource manager concepts, session commands, job submission, fault‑tolerance settings, and log inspection.

Cluster DeploymentConfigurationFlink
0 likes · 11 min read
Deploying Apache Flink Clusters: Standalone and YARN Modes
Big Data Technology & Architecture
Big Data Technology & Architecture
Feb 25, 2019 · Big Data

Understanding Flink DataSetAPI and DataStreamAPI

This article introduces Apache Flink's DataSetAPI and DataStreamAPI, explains their source, transformation, and sink concepts, highlights the key differences in transformation handling, and notes the series' goal of publishing over 500 big‑data tutorials for learners from beginner to expert.

DataSetAPIDataStreamAPIFlink
0 likes · 2 min read
Understanding Flink DataSetAPI and DataStreamAPI
Qunar Tech Salon
Qunar Tech Salon
Feb 20, 2019 · Big Data

Building Real-Time User Behavior Engineering with Apache Flink: Architecture, Features, and Implementation

This article introduces the design and implementation of a real‑time user behavior engineering platform at Qunar using Apache Flink, covering Flink's core characteristics, distributed runtime, DataStream programming model, fault‑tolerance, back‑pressure handling, event‑time processing, windowing, watermarks, and practical code examples for filtering, splitting, joining, and state management.

CheckpointDataStreamEventTime
0 likes · 18 min read
Building Real-Time User Behavior Engineering with Apache Flink: Architecture, Features, and Implementation
Big Data Technology & Architecture
Big Data Technology & Architecture
Feb 15, 2019 · Big Data

Big Data Mastery Roadmap

This article outlines a comprehensive series of over 500 planned tutorials covering Java advanced features, distributed theory, Hadoop, Spark, Flink, and various big‑data storage and processing technologies, designed to guide engineers transitioning into big‑data development from fundamentals to expert level.

FlinkHadoopJava
0 likes · 4 min read
Big Data Mastery Roadmap
NetEase Game Operations Platform
NetEase Game Operations Platform
Jan 25, 2019 · Big Data

Understanding Exactly-Once Semantics in Apache Flink: Challenges and Implementation

This article analyzes the difficulties of achieving exactly-once delivery in Apache Flink, explains the distinction between state and end‑to‑end semantics, and details how idempotent and transactional sinks—illustrated with the Bucketing File Sink—realize exactly‑once guarantees through checkpoint‑based two‑phase commit.

FlinkState ManagementStream Processing
0 likes · 13 min read
Understanding Exactly-Once Semantics in Apache Flink: Challenges and Implementation
Youzan Coder
Youzan Coder
Jan 16, 2019 · Big Data

How Youzan Scaled Real‑Time Analytics with Flink: Architecture, Pitfalls, and Lessons

This article walks through Youzan's real‑time platform architecture, explains why Flink was chosen over Spark Structured Streaming, details practical challenges such as container over‑provisioning and monitoring overhead, shares solutions for Spring integration and async caching, and outlines future directions for SQL‑based streaming and scheduler improvements.

FlinkMonitoringSQL
0 likes · 19 min read
How Youzan Scaled Real‑Time Analytics with Flink: Architecture, Pitfalls, and Lessons
Architects Research Society
Architects Research Society
Dec 30, 2018 · Big Data

Overview of Major Apache Big Data Processing Frameworks

This article provides a concise overview of numerous Apache open‑source projects—including Ignite, MapReduce, Pig, JAQL, Spark, Storm, Flink, Apex, REEF, Twill, and Beam—that enable distributed in‑memory storage, real‑time and batch processing, and advanced analytics for large‑scale data workloads.

ApacheDistributed ComputingFlink
0 likes · 22 min read
Overview of Major Apache Big Data Processing Frameworks
Alibaba Cloud Developer
Alibaba Cloud Developer
Dec 24, 2018 · Artificial Intelligence

Explore Alibaba’s New Open‑Source AI Framework, Massive Cluster Dataset, and Blink Engine

This newsletter introduces Alibaba’s X‑DeepLearning deep‑learning framework, releases a large‑scale cluster dataset for research, announces the upcoming open‑source of the Blink streaming engine, showcases the futuristic FlyZoo Hotel, and shares a welcome letter to new hires, highlighting cutting‑edge technologies across AI, big data, and cloud operations.

AICluster DataFlink
0 likes · 7 min read
Explore Alibaba’s New Open‑Source AI Framework, Massive Cluster Dataset, and Blink Engine
Didi Tech
Didi Tech
Dec 18, 2018 · Big Data

Evolution and Architecture of Didi's Real-Time Computing Platform

From early self‑built Storm and Spark Streaming clusters to a unified YARN‑based Spark platform and finally a low‑latency Flink system with extended CEP and StreamSQL capabilities, Didi’s real‑time computing platform evolved through three stages, delivering multi‑tenant isolation, rich SQL processing, and dramatically reduced development costs.

CEPFlinkReal-Time Computing
0 likes · 9 min read
Evolution and Architecture of Didi's Real-Time Computing Platform
DataFunTalk
DataFunTalk
Dec 18, 2018 · Big Data

Flink-based Real-time Data Warehouse Practice at Yanxuan

This talk presents Yanxuan’s real‑time data warehouse built on Flink, covering background challenges, overall architecture and implementation, data quality measures, monitoring, and practical application scenarios, while highlighting design goals of flexibility, high development efficiency, and stringent data quality requirements.

FlinkReal-time Data WarehouseStreaming
0 likes · 14 min read
Flink-based Real-time Data Warehouse Practice at Yanxuan
Xianyu Technology
Xianyu Technology
Nov 6, 2018 · Big Data

Technical Evolution of Xianyu Real-Time Selection System for Double Eleven

To meet Double‑Eleven’s sub‑second, billion‑item feed demands, Alibaba’s Xianyu selection system evolved from a Solr‑based search pipeline through offline batch and PostgreSQL attempts to a Blink‑powered real‑time stream platform using Niagara’s low‑latency LSM storage, delivering high‑throughput, personalized product feeds.

FlinkXianyualibaba
0 likes · 23 min read
Technical Evolution of Xianyu Real-Time Selection System for Double Eleven
dbaplus Community
dbaplus Community
Nov 1, 2018 · Big Data

How Vipshop Scales Real‑Time Data with Flink on Kubernetes

This article details Vipshop's real‑time platform architecture, the migration from Storm and Spark to Flink, Flink's deployment on Kubernetes, and the latest Unified Data Management system that unifies data access across Kafka, Redis, Tair and HDFS.

FlinkKubernetesUDM
0 likes · 12 min read
How Vipshop Scales Real‑Time Data with Flink on Kubernetes
ITPUB
ITPUB
Oct 23, 2018 · Big Data

How Meituan Built a Scalable Real‑Time Data Warehouse with Flink

This article explains how Meituan tackled growing real‑time data demands by redesigning its streaming platform, adopting a layered real‑time data warehouse architecture, selecting storage and compute technologies such as Cellar, Elasticsearch, Druid and Flink, and sharing practical tips on dimension expansion, joins, and aggregation to achieve higher throughput and lower latency.

FlinkMeituanReal-time Data Warehouse
0 likes · 15 min read
How Meituan Built a Scalable Real‑Time Data Warehouse with Flink
21CTO
21CTO
Oct 19, 2018 · Big Data

How Meituan Scales Real‑Time Computing with Flink: Architecture, Challenges & Solutions

This article summarizes Meituan’s real‑time computing platform, detailing its layered architecture built on Kafka, Flink on YARN, state management, resource isolation, fault tolerance, monitoring, and the Petra metric aggregation system, while highlighting the challenges faced and the solutions implemented to achieve high‑throughput, low‑latency stream processing at massive scale.

FlinkMonitoringResource Isolation
0 likes · 18 min read
How Meituan Scales Real‑Time Computing with Flink: Architecture, Challenges & Solutions
Meituan Technology Team
Meituan Technology Team
Oct 18, 2018 · Big Data

Building a Real-Time Data Warehouse with Flink at Meituan

Meituan replaced its Storm‑based pipeline with a four‑layer real‑time data warehouse powered by Flink, using hybrid storage (Cellar KV, Elasticsearch, Druid, MySQL) to deliver low‑latency, high‑throughput services, dramatically simplifying SQL‑driven development, unifying metrics, cutting compute costs, and paving the way for offline‑grade accuracy and reliability.

FlinkMeituanReal-time Data Warehouse
0 likes · 16 min read
Building a Real-Time Data Warehouse with Flink at Meituan
Tencent Cloud Developer
Tencent Cloud Developer
Sep 6, 2018 · Big Data

Real-Time Stream Computing: Concepts, Challenges, and Tencent Cloud Solutions

As mobile and IoT data surge, real-time stream computing—especially Flink’s low-latency, high-throughput, exactly-once engine—addresses challenges of latency, accuracy, and usability, and Tencent Cloud’s managed Flink service provides elastic, secure, integrated pipelines for applications ranging from online status monitoring to fraud detection and smart transportation.

Apache StormFlinkSpark Streaming
0 likes · 30 min read
Real-Time Stream Computing: Concepts, Challenges, and Tencent Cloud Solutions
Alibaba Cloud Developer
Alibaba Cloud Developer
Aug 13, 2018 · Big Data

How Ele.me Evolved Its Real‑Time Engine: From Storm to Flink

This article examines Ele.me’s big‑data platform evolution, comparing Storm, Spark Streaming, Structured Streaming, and Flink, detailing their architectures, consistency semantics, performance trade‑offs, and why Flink became the preferred real‑time computation engine for the company.

FlinkSparkStorm
0 likes · 15 min read
How Ele.me Evolved Its Real‑Time Engine: From Storm to Flink
Meitu Technology
Meitu Technology
Aug 2, 2018 · Big Data

Spark Streaming vs Flink – Architecture, Scheduling & Fault Tolerance

This article compares Spark Streaming and Flink across runtime models, component roles, programming APIs, task scheduling, time semantics, dynamic Kafka partition detection, fault‑tolerance mechanisms, exactly‑once guarantees, and back‑pressure handling, providing code examples and practical insights for real‑time data processing.

Dynamic Partition DetectionFlinkKafka Integration
0 likes · 23 min read
Spark Streaming vs Flink – Architecture, Scheduling & Fault Tolerance
JD Tech Talk
JD Tech Talk
Aug 2, 2018 · Big Data

Real-Time Order Statistics with Apache Flink in a Data Aggregation Platform

This article explains how the data aggregation platform adopts Apache Flink for high‑throughput, low‑latency stream processing, covering the complete workflow from data source integration, transformation operations, windowing and time concepts, to a concrete order‑count example with custom aggregation logic.

Apache FlinkEvent TimeFlink
0 likes · 10 min read
Real-Time Order Statistics with Apache Flink in a Data Aggregation Platform
ITPUB
ITPUB
Jun 14, 2018 · Big Data

Why Suning.com Sticks with Hadoop: Insights into China’s Big Data Platform Choices

Amid declining Hadoop usage reports, Suning.com’s 2018‑2020 big‑data platform case study reveals why the retailer still relies on Hadoop’s mature ecosystem, how it integrates HDFS, HBase, YARN, Hive, Spark, Flink and emerging tools, and what future resource‑management plans it envisions.

FlinkHadoopSpark
0 likes · 11 min read
Why Suning.com Sticks with Hadoop: Insights into China’s Big Data Platform Choices
Ctrip Technology
Ctrip Technology
Jun 4, 2018 · Big Data

Real-Time Data Processing Frameworks and Kafka Practices at Ctrip Ticketing

This article examines Ctrip Ticket's real-time data processing ecosystem, comparing batch and streaming frameworks such as Hadoop, Spark, Storm, Flink, and Spark Streaming, detailing Kafka deployment and configuration, and describing how these technologies are applied in production for log analysis, seat‑occupancy detection, and anti‑crawling.

FlinkSpark StreamingStorm
0 likes · 12 min read
Real-Time Data Processing Frameworks and Kafka Practices at Ctrip Ticketing
iQIYI Technical Product Team
iQIYI Technical Product Team
Jan 31, 2018 · Big Data

Evolution of iQIYI Real-Time Big Data Collection System

iQIYI’s big‑data collection system has progressed from simple HTTP log uploads to a Flume‑Kafka pipeline and finally to a custom Venus‑Agent architecture with centralized configuration, persistent offsets, dual‑Kafka streams and Flink processing, now handling tens of millions of queries per second and over three hundred billion records daily to power its AI‑driven services.

FlinkFlumeKafka
0 likes · 15 min read
Evolution of iQIYI Real-Time Big Data Collection System
Architecture Digest
Architecture Digest
Dec 16, 2017 · Big Data

Performance Comparison of Apache Flink and Apache Storm for Real‑Time Stream Processing

This report presents a systematic performance evaluation of Apache Flink and Apache Storm across multiple real‑time processing scenarios, measuring throughput, latency, message‑delivery semantics, and state‑backend effects, and provides recommendations for selecting the most suitable engine based on the observed results.

FlinkReal-Time AnalyticsStorm
0 likes · 21 min read
Performance Comparison of Apache Flink and Apache Storm for Real‑Time Stream Processing
21CTO
21CTO
Aug 14, 2017 · Big Data

Unveiling Flink’s Multi‑Layer Execution Graph: From StreamGraph to Physical Deployment

This article explains Flink’s architecture, detailing the roles of Client, JobManager and TaskManager, walks through a SocketTextStreamWordCount example, and clarifies the four‑layer graph model—StreamGraph, JobGraph, ExecutionGraph, and the physical execution graph—highlighting why each layer exists.

Execution GraphFlinkJobManager
0 likes · 9 min read
Unveiling Flink’s Multi‑Layer Execution Graph: From StreamGraph to Physical Deployment
Architecture Digest
Architecture Digest
Aug 13, 2017 · Big Data

Understanding Flink Architecture, Job Example, and Execution Graph Layers

This article explains Flink’s cluster architecture, the roles of Client, JobManager and TaskManager, demonstrates a SocketTextStreamWordCount example, and details the four-layer execution graph (StreamGraph, JobGraph, ExecutionGraph, Physical Execution Graph) to illustrate how Flink schedules and runs streaming jobs.

Execution GraphFlinkJobManager
0 likes · 9 min read
Understanding Flink Architecture, Job Example, and Execution Graph Layers
Alibaba Cloud Developer
Alibaba Cloud Developer
Aug 4, 2017 · Big Data

From PhD to Alibaba: How a Real‑Time Streaming Expert Built Blink on Flink

Alibaba algorithm engineer Shi Xiaogang shares his journey from a Peking University PhD researching real‑time iterative computation on data streams to developing Blink’s state management and recovery features in Flink, highlighting the challenges of transitioning from academia to industry and the impact of large‑scale real‑time systems.

Algorithm EngineeringFlinkcareer interview
0 likes · 13 min read
From PhD to Alibaba: How a Real‑Time Streaming Expert Built Blink on Flink
Alibaba Cloud Developer
Alibaba Cloud Developer
Jun 8, 2017 · Big Data

Flink Forward 2017: Stream Processing Insights from Alibaba, Uber & Netflix

The article recounts the 2017 Flink Forward conference in San Francisco, highlighting key sessions from DataArtisans, Uber, Netflix and Alibaba, and discusses real‑time stream processing use cases, large‑scale deployments, runtime and TableAPI/SQL improvements, and the growing adoption of Flink in the industry.

Apache FlinkFlinkReal-Time Analytics
0 likes · 16 min read
Flink Forward 2017: Stream Processing Insights from Alibaba, Uber & Netflix
Alibaba Cloud Developer
Alibaba Cloud Developer
May 25, 2017 · Big Data

How Alibaba’s Blink Engine Redefines Real‑Time Big Data Processing

This article explains how Alibaba’s Blink, built on Apache Flink, transforms batch‑oriented big‑data platforms into a unified, high‑performance real‑time computing engine, detailing its architecture, state management, checkpointing, and successful deployment in e‑commerce, search, recommendation, and online machine‑learning scenarios.

FlinkReal-Time ComputingStream Processing
0 likes · 17 min read
How Alibaba’s Blink Engine Redefines Real‑Time Big Data Processing
Architect
Architect
Jun 10, 2016 · Big Data

Understanding Session Windows in Apache Flink

This article explains the concept, implementation, and underlying mechanics of session windows in Apache Flink, covering how Flink assigns and merges windows, the relevant APIs such as SessionWindows.withGap, and detailed source code analysis for both the window assigner and trigger handling.

FlinkMergingWindowAssignersession window
0 likes · 13 min read
Understanding Session Windows in Apache Flink
Architect
Architect
May 25, 2016 · Big Data

How Flink Manages Memory to Overcome JVM Limitations

The article explains how Flink tackles JVM memory challenges by using proactive memory management, a custom serialization framework, cache‑friendly binary operations, and off‑heap memory techniques to reduce GC pressure, avoid OOM, and improve performance in big‑data workloads.

FlinkJVMOff‑Heap
0 likes · 17 min read
How Flink Manages Memory to Overcome JVM Limitations
21CTO
21CTO
Nov 19, 2015 · Big Data

Beyond Hadoop: Modern Big Data Platforms and Technologies Explained

This article surveys the evolution of Hadoop and its ecosystem, explains core storage and processing concepts, and introduces contemporary big‑data technologies such as Spark, Flink, Kafka, Lambda architecture, NoSQL databases, and cloud‑native solutions, highlighting their roles and trade‑offs.

FlinkHadoopKafka
0 likes · 17 min read
Beyond Hadoop: Modern Big Data Platforms and Technologies Explained
Efficient Ops
Efficient Ops
Oct 14, 2015 · Big Data

Spark vs Hadoop, Flink, HBase/Cassandra, Kafka & Tachyon: Expert Q&A

During a lively “Sit and Discuss” session, experts compared Spark and Hadoop, evaluated Flink against Spark, contrasted HBase with Cassandra, explained why Kafka (and sometimes Flink) is preferred for distributed messaging, and shared insights on Tachyon’s role in modern big‑data ecosystems.

CassandraFlinkHBase
0 likes · 10 min read
Spark vs Hadoop, Flink, HBase/Cassandra, Kafka & Tachyon: Expert Q&A