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Flink series (6) -- Flink window model

In most scenarios, the data flow we need to count is unbounded, so we cannot wait for the entire data flow to terminate before counting. Usually, we only need to perform statistical analysis on a certain time range or quantity range of data: for example, every five minutes count the clicks on all items in the past hour; Or for every 1,000 clicks, count the percentage of clicks per item. In the...

Flink kafka source & Sink source code analysis

Here's how the two processes link up. The most important thing here is (CTX); FlinkKafkaConsumer (FlinkKafkaConsumer); FlinkKafkaConsumer (FlinkKafkaConsumer); FlinkKafkaConsumer (FlinkKafkaConsumer);

Flink+Kafka: Implement fraud detection

First we need a continuous stream of data, generating a lot of random consumption data. Sh start and./ stop can be used to start and stop the Zk kafka service respectively. All about kafka...

The second article | Flink DataSet API programming guide

Flink's biggest highlight is real-time processing. Flink sees batch processing as a special case of streaming processing, where batch and streaming data can be processed through a single engine, and Flink will focus more resources on batch streaming fusion in the future. I introduced the use of the DataStream API in the Flink DataStream API programming guide, and in this article I will cover F...

Flink each major version function description

| | | Flink version Kafka version ElasticSearch version shows | | : - | : -- | -- - | : - | 1.0.0 | > = 0.8 x | | 1. Support scala2. Support Java8lambda | 1.2.0 | > =

Introduction to Flink sort-shuffle implementation

Flink is a big data computing engine integrating batch and stream. Large-scale batch data processing is also an important part of Flink's data processing capability. Sort-shuffle makes Flink more adept at handling large batch data processing tasks

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