How to use Java to develop a Flink-based stream processing and batch processing application

How to use Java to develop a Flink-based stream processing and batch processing application

Abstract: Flink is a distributed stream processing engine based on event time, and also supports Batch processing. This article will introduce how to use Java language to develop a Flink-based stream processing and batch processing application, and provide corresponding code examples.

1. Background introduction
Flink is a high-performance, high-reliability stream processing engine. It has the characteristics of low latency and high throughput, and can handle unbounded data streams, batch processing and iterative calculations. and other scenarios. Flink also provides rich APIs and tools, as well as integration support with third-party systems.

2. Environment preparation
First, you need to install Java Development Kit (JDK) and Apache Flink. Make sure the environment variables are configured correctly. You can use the following command to verify whether the installation is correct:

java -version
flink --version

3. Stream processing application

3.1 Project creation
First create a new Maven project and add Flink dependence. Add the following content in the pom.xml file:

<dependencies>
    <dependency>
        <groupId>org.apache.flink</groupId>
        <artifactId>flink-streaming-java_2.11</artifactId>
        <version>1.9.3</version>
    </dependency>
</dependencies>

3.2 Data source
In Flink, the streaming data source is called Source. The following is a sample code that uses the source function to create a data stream containing the numbers 1 to 100:

DataStream<Integer> stream = env.fromCollection(Arrays.asList(1, 2, 3, ..., 100));

3.3 Data conversion and processing
Flink provides a wealth of conversion and processing functions that can process data streams Perform various operations. The following is a sample code that adds 1 to each element in the data stream and filters out even numbers:

DataStream<Integer> result = stream
    .map(new MapFunction<Integer, Integer>() {
        @Override
        public Integer map(Integer value) throws Exception {
            return value + 1;
        }
    })
    .filter(new FilterFunction<Integer>() {
        @Override
        public boolean filter(Integer value) throws Exception {
            return value % 2 == 0;
        }
    });

3.4 Result output
Flink supports outputting results to different targets, such as consoles and files , database, etc. The following is a sample code that outputs the results to the console:

result.print();

3.5 Execute the stream processing application
Finally, execute the stream processing application through the execute function:

env.execute("Stream Processing Job");

4. Batch processing application

4.1 Project Creation
Similarly, add Flink dependencies in the Maven project.

4.2 Data source
The data source for batch processing applications uses DataSet. The following is a sample code that creates a data set containing strings through the fromElements function:

ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
DataSet<String> dataSet = env.fromElements("Hello", "World");

4.3 Data conversion and processing
Flink provides conversion and processing functions similar to stream processing, which can process data sets Perform various operations. Here is a sample code that converts each string in the data set to uppercase and filters out strings with a length greater than 3:

DataSet<String> result = dataSet
    .map(new MapFunction<String, String>() {
        @Override
        public String map(String value) throws Exception {
            return value.toUpperCase();
        }
    })
    .filter(new FilterFunction<String>() {
        @Override
        public boolean filter(String value) throws Exception {
            return value.length() > 3;
        }
    });

4.4 Result Output
Similar to stream processing applications, batch processing applications also Supports outputting results to different targets.

4.5 Execute batch application
Execute batch application by calling the execute function:

result.print();

5. Summary and Outlook
This article introduces how to use Java to develop a Flink-based stream Basic steps for processing and batch applications, with corresponding code examples. Using Flink, we can quickly build high-performance, reliable stream processing and batch processing applications, and can also be integrated with other systems. I hope this article can help readers understand and master the basic methods of using Flink to develop applications and further apply them to actual projects.

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