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This information applies to Edgent releases prior to 1.2.0. See Getting Started for current releases.

What is Apache Edgent?

Edgent is an open source programming model and runtime for edge devices that enables you to analyze streaming data on your edge devices. When you analyze on the edge, you can:

  • Reduce the amount of data that you transmit to your analytics server
  • Reduce the amount of data that you store

For more information, see the Edgent overview.

Apache Edgent and streaming analytics

The fundamental building block of an Edgent application is a stream: a continuous sequence of tuples (messages, events, sensor readings, and so on).

The Edgent API provides the ability to process or analyze each tuple as it appears on a stream, resulting in a derived stream.

Source streams are streams that originate data for analysis, such as readings from a device's temperature sensor.

Streams are terminated using sink functions that can perform local device control or send information to centralized analytic systems through a message hub.

Edgent's primary API is functional where streams are sourced, transformed, analyzed or sinked though functions, typically represented as lambda expressions, such as reading -> reading < 50 || reading > 80 to filter temperature readings in Fahrenheit.


See samples for information about samples that come with Edgent.

Downloading Apache Edgent

To use Edgent, you need the Edgent JAR files, which you may obtain by completing the following steps.

  1. Locate the Edgent release you would like to use on the downloads page
  2. In the Bundles column for the desired release:
    • Click on the Binary link if you simply want to use Edgent. This is the easiest method is to get up and running as it contains a pre-built version of Edgent.
    • Click on the Source link if you would like access to the Edgent source files. If you choose this method, you must manually build Edgent yourself.
  3. Download the .tgz file from one of the mirror sites
  4. Unpack the downloaded file: tar zxvf apache-edgent-X.X.X-incubating-XXX.tgz
  5. Obtain the JARs
    • If you are using a binary bundle, then the Java 8 JARs are located in edgent-X.X.X/java8
    • If you are using a source bundle, build the source code:
      1. Install Gradle if it is not yet installed
      2. Navigate to the unpacked directory: cd edgent-X.X.X-src
      3. Run gradle to initialize the Gradle wrapper
      4. Build the code and Javadoc: ./gradlew assemble
      5. The Java 8 JARs are located in edgent-X.X.X-src/build/distributions/java8

Setting up your environment

Ensure that you are running a supported environment. For more information, see the Edgent overview. This guide assumes you're running Java 8. The Edgent Java 8 JAR files are located in either the edgent-X.X.X/java8 or edgent-X.X.X-src/build/distributions/java8 directory, depending on whether you downloaded a binary or source bundle.

  1. Create a new Java project in Eclipse, and specify Java 8 as the execution environment JRE:

  2. Include one or more Edgent JARs in your project's build path depending on what features your application uses:

    1. Include one or more of the topology providers:
    2. Include the JARs for any Edgent connectors or analytic features you use in java8/connectors and java8/analytics, respectively
    3. Include the JARs for any Edgent utility features you use:
    4. Include the JAR for the java.util.logging framework for SLF4J: java8/ext/slf4j-jdk14-X.X.X.jar

Your environment is set up! You can start writing your first Edgent application.

Creating a simple application

If you're new to Edgent or to writing streaming applications, the best way to get started is to write a simple program.

Edgent is a framework that pushes data analytics and machine learning to edge devices. (Edge devices include things like routers, gateways, machines, equipment, sensors, appliances, or vehicles that are connected to a network.) Edgent enables you to process data locally—such as, in a car engine, on an Android phone, or on a Raspberry Pi—before you send data over a network.

For example, if your device takes temperature readings from a sensor 1,000 times per second, it is more efficient to process the data locally and send only interesting or unexpected results over the network. To simulate this, let's define a (simulated) TempSensor class:

import java.util.Random;

import org.apache.edgent.function.Supplier;

 * Every time get() is called, TempSensor generates a temperature reading.
public class TempSensor implements Supplier<Double> {
    double currentTemp = 65.0;
    Random rand;

        rand = new Random();

    public Double get() {
        // Change the current temperature some random amount
        double newTemp = rand.nextGaussian() + currentTemp;
        currentTemp = newTemp;
        return currentTemp;

Every time you call TempSensor.get(), it returns a new temperature reading. The continuous temperature readings are a stream of data that an Edgent application can process.

Our sample Edgent application processes this stream by filtering the data and printing the results. Let's define a TempSensorApplication class for the application:

import java.util.concurrent.TimeUnit;

import org.apache.edgent.providers.direct.DirectProvider;
import org.apache.edgent.topology.TStream;
import org.apache.edgent.topology.Topology;

public class TempSensorApplication {
    public static void main(String[] args) throws Exception {
        TempSensor sensor = new TempSensor();
        DirectProvider dp = new DirectProvider();
        Topology topology = dp.newTopology();
        TStream<Double> tempReadings = topology.poll(sensor, 1, TimeUnit.MILLISECONDS);
        TStream<Double> filteredReadings = tempReadings.filter(reading -> reading < 50 || reading > 80);


To understand how the application processes the stream, let's review each line.

Specifying a provider

Your first step when you write an Edgent application is to create a DirectProvider:

DirectProvider dp = new DirectProvider();

A Provider is an object that contains information on how and where your Edgent application will run. A DirectProvider is a type of Provider that runs your application directly within the current virtual machine when its submit() method is called.

Creating a topology

Additionally, a Provider is used to create a Topology instance:

Topology topology = dp.newTopology();

In Edgent, Topology is a container that describes the structure of your application:

  • Where the streams in the application come from
  • How the data in the stream is modified

In the TempSensorApplication class above, we have exactly one data source: the TempSensor object. We define the source stream by calling topology.poll(), which takes both a Supplier function and a time parameter to indicate how frequently readings should be taken. In our case, we read from the sensor every millisecond:

TStream<Double> tempReadings = topology.poll(sensor, 1, TimeUnit.MILLISECONDS);

Defining the TStream object

Calling topology.poll() to define a source stream creates a TStream<Double> instance, which represents the series of readings taken from the temperature sensor.

A streaming application can run indefinitely, so the TStream might see an arbitrarily large number of readings pass through it. Because a TStream represents the flow of your data, it supports a number of operations which allow you to modify your data.

Filtering a TStream

In our example, we want to filter the stream of temperature readings, and remove any "uninteresting" or expected readings—specifically readings which are above 50 degrees and below 80 degrees. To do this, we call the TStream's filter method and pass in a function that returns true if the data is interesting and false if the data is uninteresting:

TStream<Double> filteredReadings = tempReadings.filter(reading -> reading < 50 || reading > 80);

As you can see, the function that is passed to filter operates on each tuple individually. Unlike data streaming frameworks like Apache Spark, which operate on a collection of data in batch mode, Edgent achieves low latency processing by manipulating each piece of data as soon as it becomes available. Filtering a TStream produces another TStream that contains only the filtered tuples; for example, the filteredReadings stream.

Printing to output

When our application detects interesting data (data outside of the expected parameters), we want to print results. You can do this by calling the TStream.print() method, which prints using .toString() on each tuple that passes through the stream:


Unlike TStream.filter(), TStream.print() does not produce another TStream. This is because TStream.print() is a sink, which represents the terminus of a stream.

In addition to TStream.print() there are other sink operations that send tuples to an MQTT server, JDBC connection, file, or Kafka cluster. Additionally, you can define your own sink by invoking TStream.sink() and passing in your own function.

Submitting your application

Now that your application has been completely declared, the final step is to run your application.

DirectProvider contains a submit() method, which runs your application directly within the current virtual machine:


After you run your program, you should see output containing only "interesting" data coming from your sensor:


As you can see, all temperatures are outside the 50-80 degree range. In terms of a real-world application, this would prevent a device from sending superfluous data over a network, thereby reducing communication costs.

Further examples

This example demonstrates a small piece of Edgent's functionality. Edgent supports more complicated topologies, such as topologies that require merging and splitting data streams, or perform operations which aggregate the last N seconds of data (for example, calculating a moving average).

For more complex examples, see: