Building Applications with Apache Flink (Part 3): Stream Processing with the DataStream API

In the previous article we have written a SourceFunction to emit measurements from a CSV source file. In this article we are going to use the SourceFunction to serve a DataStream.

What we are going to build

You will see how to use the DataStream API for applying operators on the stream, so that for each station the maximum temperature for a day is identified.

Source Code

You can find the full source code for the example in my git repository at:

DataStream API

The DataStream API of Apache Flink makes it possible to apply a various operations on a stream of incoming data.

The Apache Flink documentation describes a DataStream as:

DataStream programs in Flink are regular programs that implement transformations on data streams (e.g., filtering, updating state, defining windows, aggregating). The data streams are initially created from various sources (e.g., message queues, socket streams, files). Results are returned via sinks, which may for example write the data to files, or to standard output (for example the command line terminal). Flink programs run in a variety of contexts, standalone, or embedded in other programs. The execution can happen in a local JVM, or on clusters of many machines.

Example Program: Maximum Air Temperature by station and day

In this example we are using the SourceFunction from the previous article to serve the DataStream. We are first setting the time characteristics of the DataStream to the EventTime, because each measurement carries the measurement timestamp. We are then building a KeyedStream over the DataStream, which groups the incoming data by its station. And finally we use a non-overlapping tumbling window with 1 day length, from which the maximum temperature is used.

The results in this example are written to a Console, but in the next article you will learn how to write a custom SinkFunction to write the data into a PostgreSQL database for further data analysis.

// Copyright (c) Philipp Wagner. All rights reserved.
// Licensed under the MIT license. See LICENSE file in the project root for full license information.

package app;

import model.LocalWeatherData;
import org.apache.flink.api.common.functions.FilterFunction;
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.streaming.api.TimeCharacteristic;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.datastream.KeyedStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.timestamps.AscendingTimestampExtractor;
import org.apache.flink.streaming.api.windowing.time.Time;
import stream.sources.csv.LocalWeatherDataSourceFunction;
import utils.DateUtilities;

import stream.sources.csv.LocalWeatherDataSourceFunction;
import utils.DateUtilities;

public class WeatherDataStreamingExample {

    public static void main(String[] args) throws Exception {

        final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

        // Use the Measurement Timestamp of the Event:

        // We are sequentially reading the historic data from a CSV file:

        // Path to read the CSV data from:
        final String csvStationDataFilePath = "C:\\Users\\philipp\\Downloads\\csv\\201503station.txt";
        final String csvLocalWeatherDataFilePath = "C:\\Users\\philipp\\Downloads\\csv\\201503hourly_sorted.txt";

        // Add the CSV Data Source and assign the Measurement Timestamp:
        DataStream<model.LocalWeatherData> localWeatherDataDataStream = env
                .addSource(new LocalWeatherDataSourceFunction(csvStationDataFilePath, csvLocalWeatherDataFilePath))
                .assignTimestampsAndWatermarks(new AscendingTimestampExtractor<LocalWeatherData>() {
                    public long extractAscendingTimestamp(LocalWeatherData localWeatherData) {
                        Date measurementTime = DateUtilities.from(localWeatherData.getDate(), localWeatherData.getTime(), ZoneOffset.ofHours(localWeatherData.getStation().getTimeZone()));

                        return measurementTime.getTime();

        // First build a KeyedStream over the Data with LocalWeather:
        KeyedStream<LocalWeatherData, String> localWeatherDataByStation = localWeatherDataDataStream
                // Filte for Non-Null Temperature Values, because we might have missing data:
                .filter(new FilterFunction<LocalWeatherData>() {
                    public boolean filter(LocalWeatherData localWeatherData) throws Exception {
                        return localWeatherData.getTemperature() != null;
                // Now create the keyed stream by the Station WBAN identifier:
                .keyBy(new KeySelector<LocalWeatherData, String>() {
                    public String getKey(LocalWeatherData localWeatherData) throws Exception {
                        return localWeatherData.getStation().getWban();

        // Now take the Maximum Temperature per day from the KeyedStream:
        DataStream<LocalWeatherData> maxTemperaturePerDay =
                        // Use non-overlapping tumbling window with 1 day length:
                        // And use the maximum temperature:

        env.execute("Max Temperature By Day example");


In this part of the series you have seen how to use the DataStream API to analyze data from the custom SourceFunction.

The next part of the series shows how to write a custom SinkFunction for writing the DataStream results into a PostgreSQL database.

How to contribute

One of the easiest ways to contribute is to participate in discussions. You can also contribute by submitting pull requests.

General feedback and discussions?

Do you have questions or feedback on this article? Please create an issue on the GitHub issue tracker.

Something is wrong or missing?

There may be something wrong or missing in this article. If you want to help fixing it, then please make a Pull Request to this file on GitHub.