GDELT on SCDF : Implementing a reactive source application

In the second part of our blog post series “processing GDELT data with SCDF on kubernetes” we will create a custom source application based on spring cloud stream to pull GDELT Data and use it in a very simple flow.

Source Code

You can find the source code on github:

git clone

cd gdelt-article-feed-source

maven project setup

The project will be based on Spring Cloud Stream and we wil use the spring cloud dependency management with the latest spring cloud relase Finchley.SR2:


Even if the implementation itself is not kafka-specific (more on binder abstraction) we include the Spring Cloud Kafka Binder directly in our project to build artifacts deployable on our target setup (Kubernetes + Kafka). We also add reactive programming support to leverage the Java Integration DSL for our source implementation.



Use the spring boot maven plugin to package the application itself:


Besides the actual fat jar (that will be dockerized later on) we also want to create a so called “metadata-only” jar using the Spring Cloud Stream & Task Metadata Plugin to aggregate spring boot metadata into a seperate lightweight jar. As we are using kubernetes as our deployment target we need to provide docker-based artifacts for deployment. Spring cloud data flow can not determine the actual configuration properties of an application directly from the docker image, but you can provide an additional jar besides the docker image to provide the necessary metadata about configuration options (names, descriptions, default values).


Dockerize the spring boot application using Google’s JIB Maven Plugin:



Spring Cloud Stream provides a couple of ways to implement a source application. Besides native spring cloud stream annotation you are also free to use Spring Integration or reactive apis. We chose
to implement our custom source utilizing the Spring Integration Java DSL as it resulted in very few lines of wrapper code to emit a Array Of GDELTArticle objects. Read more about reactive spring cloud sources here.

public class GDELTSourceApplication {

private GDELTSourceProperties properties;

public Publisher<Message<List<GDELTArticle>>> emit() {
   return IntegrationFlows.from(() -> {
       try {
           URL feedUrl = new URL(""
                  + URLEncoder.encode(properties.getQuery(), "UTF-8")
                  + "&mode=artlist&maxrecords=250&timespan=1h&sort=datedesc&format=json");
 "going to fetch data from using url = " + feedUrl);
           InputStream inputStreamObject = feedUrl.openStream();
           BufferedReader streamReader = new BufferedReader(new InputStreamReader(inputStreamObject, "UTF-8"));
           StringBuilder responseStrBuilder = new StringBuilder();
           String inputStr;
           while ((inputStr = streamReader.readLine()) != null) {
           JSONObject jsonObject = new JSONObject(responseStrBuilder.toString());
           JSONArray articles = jsonObject.getJSONArray("articles");
           List<GDELTArticle> response = new ArrayList<>();
           for (int i = 0; i < articles.length(); i++) {
               JSONObject article = articles.getJSONObject(i);
               GDELTArticle a = new GDELTArticle();
           return new GenericMessage<>(response);
        } catch (Exception e) {
            logger.error("", e);
            return new GenericMessage<>(null);
   }, e -> e.poller(p -> p.fixedDelay(, TimeUnit.SECONDS))).toReactivePublisher();

We want our source application to properly expose it’s configuration parameters, so we create a dedicated configuration property. The javadoc comment of the members will be displayed as the description and the initial values will automatically noted as the default values (no need to mention them in the javadoc description):

package com.syscrest.blogposts.scdf.gdeltsource;
public class GDELTSourceProperties {

 * The query to use to select data.
 * Example: ("climate change" or "global warming")
private String query = "climate change";

 * The delay between pulling data from gdelt (in seconds).
private long triggerDelay = 120L;

/* ... setter and getter omitted ... */


As Spring boot applications are aware of a lot common configuration properties, we create a file named META-INF/ to explictly limit the displayed configuration options to our class (read more about whitelisting here).


Build the project

You can package the application, create the docker image and upload it to docker hub with a single command (It requires a docker hub account, please replace the placeholders accordingly).

Note: you could skip this step and use our docker image (syscrest/gdelt-article-feed-source).

./mvnw clean package jib:build \ \ \

The output should look like this:

[INFO] Containerizing application to syscrest/gdelt-article-feed-source...
[INFO] Retrieving registry credentials for
[INFO] Building classes layer...
[INFO] Building resources layer...
[INFO] Getting base image
[INFO] Building dependencies layer...
[INFO] Finalizing...
[INFO] Container entrypoint set to [java, -cp, /app/resources/:/app/classes/:/app/libs/*, com.syscrest.blogposts.scdf.gdeltsource.GDELTSourceApplication]
[INFO] Built and pushed image as syscrest/gdelt-article-feed-source
[INFO] ------------------------------------------------------------------------
[INFO] ------------------------------------------------------------------------

The docker image has been pushed to But we also want to use the metadata jar (target/gdelt-article-feed-source-1.0.0-SNAPSHOT-metadata.jar). SCDF can pull jar files not only from maven central but also from any http server, so we uploaded it to our website to make it available for the scdf server (you can find the url in the next section).

Register the app

Browse your Spring Cloud Data Flow UI and select “Apps” and then “+ Add Application“:


Select “Register one or more applications”:

Register the app using:

  • Name: gdelt-article-feed-source
  • Type: Source
  • URI: docker:syscrest/gdelt-article-feed-source:latest
  • Metadata URI:

Afterwards browse the application list and click on “gdelt-article-feed-source”:


to verify that all configuration options have been picked up from the metadata jar file:


Creating a stream

Let’s create a simple stream that uses our custom application as the source and use the very basic log processor to just dump the messages into the logfile of a pod. Select Streams on the left sidebar and then click Create stream(s):


Just copy and paste our example to query all current articles containing ‘climate change’ into the textbox:

gdelt-article-feed-source --trigger-delay=300 --query='climate change' | log


You can also just type and use the autocompletion:


Afterwards save the stream (don’t check ‘Deploy Stream(s)’):


Locate your previously saved stream in the stream list:


When you click on deploy you can define deployment specific settings like memory and cpu assignments (not necessary , default values are sufficient):


Your spring cloud data flow instance will now deploy pods in the same namespace it’s running using the stream name plus source/processor names:

kubectl -n scdf-170 get pods

NAME                                                      READY     STATUS             RESTARTS   AGE
gdelt-stream-1-log-gdelt-article-feed-source-6c457dfb9f-v28ls   0/1       Running            0          1m
gdelt-stream-1-log-log-64cfc8bc-bb6n9                           0/1       Running            0          1m


Let’s peek into the “log” pod the see the data that has been emitted by our custom source:

kubectl -n scdf-170 logs -f gdelt-demo-1-log-7999bb94d8-9dcw6

output (reformatted for better readability):

2018-11-30 23:06:03.447  INFO 1 --- [container-0-C-1] log-sink
      "title":"Angela Merkel nach G20 : „ Forbes  kürt sie zur mächtigsten Frau der Welt - News",
      "title":"COP24 rät wegen Klimawandel zur weniger Fleischkonsum",

Let’s create a slightly improved version that splits the array of GDELTArticle into separate messages using the splitter starter app and channel these messages into an explicit topic named climate-change-articles:

gdelt-article-feed-source --query='climate change' | splitter --expression="#jsonPath(payload,'$.*')" > :climate-change-articles


If you peek into the topic you can see that each message just contains a single article:

{"url":"","title":"Biomass Power Generation Market – Global Industry Analysis , Size , Share , Growth , Trends and Forecast 2018 – 2022 – Advertising Market","language":"English","sourcecountry":"","domain":"","seendate":"20181205T120000Z"}
{"url":"","title":"In the news today , Dec . 5","language":"English","sourcecountry":"Canada","domain":"","seendate":"20181205T120000Z"}
{"url":"","title":"Manovra | rivoluzione congedo parentale Si potrà lavorare fino al parto","language":"Italian","sourcecountry":"Italy","domain":"","seendate":"20181205T120000Z"}
{"url":",-AGTF-to-provide-funds-for-NEP","title":"AfDB , AGTF to provide funds for NEP - BusinessGhana News","language":"English","sourcecountry":"Ghana","domain":"","seendate":"20181205T120000Z"}
{"url":"","title":"Daily Star Opinions : Overview with Gwyne Dyer","language":"English","sourcecountry":"Philippines","domain":"","seendate":"20181205T120000Z"}

You will notice that calling the gdelt endpoint continuously will result in a lot of duplicate articles … we will implement a filter/deduplication processor in one of the next SCDF on GDELT blog posts.

GDELT on SCDF : Bootstrapping spring cloud data flow 1.7.0 on kubernetes using kubectl

In the first part of our planned blog posts (processing GDELT data with SCDF on kubernetes) we go through the steps to deploy the latest Spring Cloud Data Flow (SCDF) Release 1.7.0 on Kubernetes , including the latest version of starter apps that will be used in the examples.

We stick to the manual steps described here in the official spring cloud dataflow documentation to deploy all components to our kubernetes cluster into a dedicated namespace scdf-170 to run the examples.

This installation will not be production-ready, it is about experimenting and to ensure compability as we experienced some incompabilities mixing own source/sink implementations based on Finchley.SR2 and the prepackaged Starter Apps based on Spring Boot 1.5 / Spring Cloud Streams 1.3.X.


Clone the git repository to retrieve the neccessary kubernetes configuration files and switch to the 1.7.0.RELEASE branch:

git clone
cd spring-cloud-dataflow-server-kubernetes
git checkout v1.7.0.RELEASE

installation with kubectl

We want to use a dedicated namespace scdf-170 for our deployment, so we create it first:

echo '{ "kind": "Namespace", "apiVersion": "v1", "metadata": { "name": "scdf-170", "labels": { "name": "scdf-170" } } }' | kubectl create -f -

Afterwards we can deploy the dependencies (kafka/mysql/redis) and the spring cloud dataflow server itself:

kubectl create -n scdf-170 -f src/kubernetes/kafka/
kubectl create -n scdf-170 -f src/kubernetes/mysql/
kubectl create -n scdf-170 -f src/kubernetes/redis/
kubectl create -n scdf-170 -f src/kubernetes/metrics/metrics-svc.yaml
kubectl create -n scdf-170 -f src/kubernetes/server/server-roles.yaml
kubectl create -n scdf-170 -f src/kubernetes/server/server-rolebinding.yaml
kubectl create -n scdf-170 -f src/kubernetes/server/service-account.yaml
kubectl create -n scdf-170 -f src/kubernetes/server/server-config-kafka.yaml
kubectl create -n scdf-170 -f src/kubernetes/server/server-svc.yaml
kubectl create -n scdf-170 -f src/kubernetes/server/server-deployment.yaml

verify and enable access

kubectl -n scdf-170 get all

Output should look like:

NAME                                READY     STATUS    RESTARTS   AGE
pod/kafka-broker-696786c8f7-fjp4p   1/1       Running   1          5m
pod/kafka-zk-5f9bff7d5-tmxzg        1/1       Running   0          5m
pod/mysql-f878678df-2t4d6           1/1       Running   0          5m
pod/redis-748db48b4f-8h75x          1/1       Running   0          5m
pod/scdf-server-757ccb576c-9fssd    1/1       Running   0          5m
NAME                  TYPE           CLUSTER-IP       EXTERNAL-IP   PORT(S)                      AGE
service/kafka         ClusterIP             9092/TCP                     5m
service/kafka-zk      ClusterIP             2181/TCP,2888/TCP,3888/TCP   5m
service/metrics       ClusterIP            80/TCP                       5m
service/mysql         ClusterIP            3306/TCP                     5m
service/redis         ClusterIP              6379/TCP                     5m
service/scdf-server   LoadBalancer        80:30884/TCP                 5m
NAME                           DESIRED   CURRENT   UP-TO-DATE   AVAILABLE   AGE
deployment.apps/kafka-broker   1         1         1            1           5m
deployment.apps/kafka-zk       1         1         1            1           5m
deployment.apps/mysql          1         1         1            1           5m
deployment.apps/redis          1         1         1            1           5m
deployment.apps/scdf-server    1         1         1            1           5m
NAME                                      DESIRED   CURRENT   READY     AGE
replicaset.apps/kafka-broker-696786c8f7   1         1         1         5m
replicaset.apps/kafka-zk-5f9bff7d5        1         1         1         5m
replicaset.apps/mysql-f878678df           1         1         1         5m
replicaset.apps/redis-748db48b4f          1         1         1         5m
replicaset.apps/scdf-server-757ccb576c    1         1         1         5m

As the scdf server will automatically generate a random password for the user “user” on first startup, we need to grep the log output (using your individual scdf-service pod name, see output of previous command):

kubectl logs -n scdf-170 scdf-server-757ccb576c-9fssd | grep "Using default security password"

output should look like

Using default security password: 8d6b58fa-bf1a-4a0a-a42b-c7b03ab15c60 with roles 'VIEW,CREATE,MANAGE'

To access the UI (and rest-api for the cli) you can create a port-forward to the scdf-server pod (using your individual scdf-service pod name, see output of “kubectl -n scdf-170 get all”):

kubectl -n scdf-170 port-forward scdf-server-757ccb576c-9fssd 2345:80

where 2345 would be the local port on your machine where you can now access the ui and the rest-api.

Install the dataflow cli

You can download the jar file directly from spring’s maven repository:


and start the cli:

java -jar spring-cloud-dataflow-shell-1.7.0.RELEASE.jar \
--dataflow.username=user \
--dataflow.password= \

The output should look like this:

 / ___| _ __  _ __(_)_ __   __ _   / ___| | ___  _   _  __| |
 \___ \| '_ \| '__| | '_ \ / _` | | |   | |/ _ \| | | |/ _` |
  ___) | |_) | |  | | | | | (_| | | |___| | (_) | |_| | (_| |
 |____/| .__/|_|  |_|_| |_|\__, |  \____|_|\___/ \__,_|\__,_|
  ____ |_|    _          __|___/                 __________
 |  _ \  __ _| |_ __ _  |  ___| | _____      __  \ \ \ \ \ \
 | | | |/ _` | __/ _` | | |_  | |/ _ \ \ /\ / /   \ \ \ \ \ \
 | |_| | (_| | || (_| | |  _| | | (_) \ V  V /    / / / / / /
 |____/ \__,_|\__\__,_| |_|   |_|\___/ \_/\_/    /_/_/_/_/_/
Welcome to the Spring Cloud Data Flow shell. For assistance hit TAB or type "help".

Install starter apps

After the initial installation there are no applications (task/stream) registed. As you can see here (for stream apps), you need to pick a explicit combination of the packaging format (jar vs docker) and the messaging technology (Kafka vs RabbitMQ).

We will go for docker (because we are running on Kubernetes) and Kafka 0.10 as the messaging layer based on newer Spring Boot and Spring Cloud Stream versions (2.0.x + 2.0.x).

We can register all the available starter apps with a single cli command:

dataflow:>app import --uri


Successfully registered .........
...... processor.pmml.metadata, sink.router.metadata, sink.mongodb]

and the Spring Cloud Task Starter Apps (based on Spring Boot 2.0.x + Spring Cloud Task 2.0.x):

dataflow:> app import --uri

Successfully registered ..........
........ task.timestamp, task.timestamp.metadata]

You can verify the installation by listing all available applications:

dataflow:>app list

The Output should look like this:

║app│    source    │         processor         │           sink           │        task        ║
║   │file          │bridge                     │aggregate-counter         │composed-task-runner║
║   │ftp           │filter                     │counter                   │timestamp           ║
║   │gemfire       │groovy-filter              │field-value-counter       │timestamp-batch     ║
║   │gemfire-cq    │groovy-transform           │file                      │                    ║
║   │http          │grpc                       │ftp                       │                    ║
║   │jdbc          │header-enricher            │gemfire                   │                    ║
║   │jms           │httpclient                 │hdfs                      │                    ║
║   │load-generator│image-recognition          │jdbc                      │                    ║
║   │loggregator   │object-detection           │log                       │                    ║
║   │mail          │pmml                       │mongodb                   │                    ║
║   │mongodb       │python-http                │mqtt                      │                    ║
║   │mqtt          │python-jython              │pgcopy                    │                    ║
║   │rabbit        │scriptable-transform       │rabbit                    │                    ║
║   │s3            │splitter                   │redis-pubsub              │                    ║
║   │sftp          │tasklaunchrequest-transform│router                    │                    ║
║   │syslog        │tcp-client                 │s3                        │                    ║
║   │tcp           │tensorflow                 │sftp                      │                    ║
║   │tcp-client    │transform                  │task-launcher-cloudfoundry│                    ║
║   │time          │twitter-sentiment          │task-launcher-local       │                    ║
║   │trigger       │                           │task-launcher-yarn        │                    ║
║   │triggertask   │                           │tcp                       │                    ║
║   │twitterstream │                           │throughput                │                    ║
║   │              │                           │websocket                 │                    ║

Continue reading on how to implement a custom source application using the reactive framework on spring cloud streams in our next blog post: Implementing a custom reactive source application for spring cloud data flow.

Blog post series: Processing feeds with Spring Cloud Data Flow on Kubernetes

We are starting a blog post series to dig deeper into the capabilities of Spring Cloud Data Flow (SCDF) running on Kubernetes. This blog post will be updated when new posts have been published.

List of blog posts for quick access:


Let’s have a look at the data we want to process:

Supported by Google Jigsaw, the GDELT Project monitors the world’s broadcast, print, and web news from nearly every corner of every country in over 100 languages and identifies the people, locations, organizations, themes, sources, emotions, counts, quotes, images and events driving our global society every second of every day, creating a free open platform for computing on the entire world.

Besides raw data feeds there are powerful APIs to query and even to visualize the GDELT datasets. This Blog post is inspired by the “rss feed for web archiving coverage about climate change” taken from a blog post on

This searches for all articles published in the last hour mentioning
“climate change” or “global warming” and returns the first 200 articles,
ordered by date with the newest articles first and returned as an RSS feed
that includes the primary URL of each article as one item and, as a separate
item, the URL of the mobile/AMP edition of the page, if available.
This demonstrates how to use the API as a data source for web archiving.

We want to use this type of query to pull the latest articles for a configurable query from the GDELT project and do some complex processing on Spring Cloud Data Flow (SCDF). We will download the articles, do some analysis on the content, store and also visualize it.

About Spring Cloud Data Flow (SCDF)

Taken from

Spring Cloud Data Flow is a toolkit for building data integration
and real-time data processing pipelines.

Pipelines consist of Spring Boot apps, built using the Spring Cloud Stream
or Spring Cloud Task microservice frameworks. This makes Spring Cloud Data Flow
suitable for a range of data processing use cases, from import/export to
event streaming and predictive analytics.

Part 1 : Setting up the runtime environment

As there are multiple platform implementations available, it can run your streams/tasks on a Local Server, on CloudFoundry, on Kubernetes, on YARN and even MESOS

As these different implementations require different packaging (jar vs docker), we went for kubernetes as the platform and Kafka as the messaging solution in our examples.

You can find instructions to install Spring Cloud Data Flow in our first blog post about GDELT on SCDF: Bootstrapping SCDF on Kubernetes using KUBECTL.

Part 2 : How to pull gdelt data into SCDF

Continue reading on how to implement a custom source application using the reactive framework on spring cloud streams: Implementing a custom reactive source application for spring cloud data flow.

Part 3 : How to filter duplicates

The next planned blog post will continue to enhance your first stream definition by adding a custom processor to drop duplicate articles. Stay tuned.