In front of the large screen of data monitoring at 3 a.m., the technical director of an e-commerce platform suddenly discovered an abnormal fluctuation: the payment success rate dropped by 15%. The traditional data warehouse is still sleeping at this time, and the real-time risk control system built on Flink has already captured this signal and automatically triggered the early warning mechanism. When the operation and maintenance team arrived, the system had completed abnormal transaction interception, automatic switching of service nodes, and pushing user compensation plans. This is not a science fiction scenario, but the real ability given to the company by Flink.
1. Big Data Cognitive Revolution
What is big data
Big data is the "three-body problem" in the data field, referring to a collection of data that cannot be captured, managed and processed in a reasonable time using traditional data processing tools. Its core features are defined by 4V:
- Volume: The data scale reaches the ZB level (1 ZB = 1 billion TB). For example, 25 EB of data are generated every day in the world, equivalent to 2.5 billion high-definition movies.
- Velocity: The data generation speed is extremely fast, such as particle collision experiments that produce PB-level data per second.
- Variety: Structured data accounts for only 20%, and the rest are unstructured data such as logs, pictures, and videos.
- Value density (Value): The proportion of effective information is extremely low, and value needs to be refined through complex mining (for example, useful clips in surveillance videos may only account for 0.01%).
Technology evolution timeline
Google released GFS paper in 2003 → Hadoop was born in 2006 → Spark appeared in 2011 → Flink was released in 2014 → Kubernetes integration in 2019.
Big data technology ecosystem
Storage layer: HDFS, S3, HBase, Iceberg
Computation layer: MapReduce, Spark, Flink, Presto
Message system: Kafka, Pulsar, RocketMQ
Resource Scheduling: YARN, Kubernetes, Mesos
Data Services: Hive, Hudi, Doris, ClickHouse
2. The law of survival in the data torrent era
When 25EB of data is generated every day in the world (equivalent to 2.5 billion high-definition movies), traditional data processing systems are like salvageing the ocean with bamboo baskets. Banks have tens of thousands of transaction records per second, millions of interactive data per minute on social platforms, and millisecond-level sensor readings of IoT devices, these data torrents are reshaping the rules of the business world.
The evolutionary history of distributed computing architecture is a history of confrontation with data expansion:
- Batch processing era: Hadoop uses MapReduce to realize the parallelization of "data porters"
- Stream treatment germination period: Storm sets a precedent for real-time processing, but is limited by the lack of Exactly-Once
- Hybrid architecture period: Lambda architecture attempts to make up for the gap with batch flow combination, but it brings double development costs
- The era of unified computing: Flink's integrated flow-in architecture ends this evolutionary competition
Architectural model comparison
| Architecture Type | Processing delay | Typical scenarios | Representative technology |
|---|---|---|---|
| Batch Processing Architecture | Hour level | Offline Reports/Historical Analysis | Hadoop+Hive |
| Lambda architecture | Minute level | Real-time and accuracy take into account scenarios | Storm+HDFS |
| Kappa architecture | Seconds | Pure real-time streaming | Kafka+Flink |
| Integrated flow batch architecture | Millisecond level | Complex event processing | Flink |
Example of computing mode evolution
Batch processing (Spark):
JavaRDD textFile = ("hdfs://");
JavaRDD counts = (line -> ((" ")))
.map(word -> 1)
.reduceByKey((a, b) -> a + b);
Stream processing (Flink):
DataStream events = (new KafkaSource());
(event -> ())
.window(((5)))
.sum("clicks");
3. Flink's disruptive innovation
Apache Flink means "agile" in German, aptly interpreting its core strengths. This computing engine, born at the University of Technology of Berlin, breaks through the three major barriers of stream computing with a unique architectural design:
1. Time Magician
// Exquisite difference between event time and processing time
DataStream<Event> stream = env
.addSource(new KafkaSource())
.assignTimestampsAndWatermarks(
WatermarkStrategy
.<Event>forBoundedOutOfOrderness((5))
.withTimestampAssigner((event, timestamp) -> ())
);Through the Watermark mechanism, Flink can handle out-of-order events like manipulating timelines, reconstructing accurate time dimensions in real-time calculations.
2. Status Alchemy
Traditional stream processing systems such as Storm push state management to external storage, but Flink has built-in state memory:
- Operator State: Local memory of each operator
- Keyed State: Partition memory based on data keys
- State Backend: Plugable storage policy (memory/RocksDB)
- This design allows the throughput to be increased by more than 10 times when processing stateful computing.
3. Fault-tolerant barrier
Based on the distributed snapshot of Chandy-Lamport algorithm, Flink implements:
- Exactly-Once
- Sub-second fault recovery
- Zero data loss
Comparative tests show that in the node failure scenario, Flink's recovery speed is 20 times faster than Storm and 5 times faster than Spark Streaming.
4. Flink's stars and sea
From Alibaba's double 11 trillion-level real-time large screens to Uber's dynamic pricing system; from Netflix's real-time content recommendations to Ping An Bank's real-time anti-fraud detection, Flink is reshaping these scenarios:
Real-time warehouse architecture evolution
Traditional architecture:
Business System -> Kafka -> Spark Batch Processing -> Hive -> Reporting System (T+1)
Flink architecture:
Business System -> Kafka -> Flink Real-time ETL -> Kafka -> Flink Real-time Analysis -> Real-time Large Screen (Second Delay)
After a retail company moved, the evaluation of the promotional performance was advanced from the next day to real-time, and the inventory turnover rate increased by 37%.
A new paradigm for machine learning
Implemented through the Flink ML library:
Real-time feature engineering
Online model training
Prediction results streaming feedback
A certain video platform shortened the recommended model update frequency from the day level to the minute level, and increased the CTR by 15%.
This series will take you to start with the installation and deployment of Flink, gradually penetrate into the core areas such as window mechanism, state management, and CEP complex event processing, and finally reach the pinnacle of integrated flow batch architecture design. When you complete this journey, you will have the magic of turning the "cold flow" of data into a "hot spring", allowing enterprises to truly have the super power of "pivot" in the era of big data.
Source text from:
Source code address:/daimajiangxin/flink-learning