Earlier I did a workshop at Ordina in order to introduce my colleagues to the wonderful world of stream processing. For that workshop I used traffic data, since especially in Belgium, traffic data is something everybody can easily relate to as we all have to endure it every workday.

Table of content

  1. Introduction
  2. The Data
  3. Native Java Stream Processing
  4. Kafka Streams with Spring Kafka
  5. Apache Storm
  6. Conclusion


In this blog post we will use traffic data made available by the Flemish government.

Several examples will be provided about how this data can be processed in various ways:

The Data

The traffic data is registered on fixed sensors installed in the road itself.


General information about the sensors can be retrieved from http://miv.opendata.belfla.be/miv/configuratie/xml.

    <meetpunt unieke_id="3640">
        <volledige_naam>Parking Kruibeke</volledige_naam>

It is pretty static as these sensors do not tend to move themselves.

Every minute the latest sensor output is published on http://miv.opendata.belfla.be/miv/verkeersdata.

This is one big XML file containing all the aggregated data of every sensor of the last minute.

    <meetpunt beschrijvende_id="H211L10" unieke_id="1152">
        <meetdata klasse_id="1">
        <meetdata klasse_id="2">
        <meetdata klasse_id="3">
        <meetdata klasse_id="4">
        <meetdata klasse_id="5">

For more information (in Dutch) about this dataset you can go to https://data.gov.be/nl/dataset/7a4c24dc-d3db-460a-b73b-cf748ecb25dc. Over there you will also find the XSD files describing the XML structure.

Transform to Events

Since I am using Spring Boot to kickstart the application, you can go to https://start.spring.io/ to get started. Some handy baseline dependencies to get started are: Web, Actuator and DevTools.

Because the data is provided in a single XML file, we will transform it into separate events per sensor. This brings it also inline with how true sensory events would arrive within our system if we would not be dealing with a big XML file.

A small Spring Cloud Stream application will be built to read in the XML, transform it to events and push these events to a Kafka topic.

You might wonder, why would we use Spring Cloud Stream for this? It makes it very easy to read/write messages to Kafka with it.

Add the appropriate starter:


Define a Spring Boot application - make sure to enable scheduling.

    public class OpenDataTrafficApplication {

        public static void main(String[] args) {
            SpringApplication.run(OpenDataTrafficApplication.class, args);

Define some input and output topics.

    public interface Channels {

        SubscribableChannel trafficEvents();

        MessageChannel trafficEventsOutput();

        MessageChannel sensorDataOutput();

Create a bean to read in the events.

    public List<TrafficEvent> readInData() throws Exception {
        log.info("Will read in data from " + url);

        JAXBContext jc = JAXBContext.newInstance("generated.traffic");
        Unmarshaller um = jc.createUnmarshaller();

        Miv miv = (Miv) um.unmarshal(new URL(url).openStream());

        log.info(" This data is from " + miv.getTijdPublicatie().toGregorianCalendar().getTime());
        List<TrafficEvent> trafficEventList = convertXmlToDomain.trafficMeasurements(miv.getMeetpunt());

        lastReadInDate = miv.getTijdPublicatie().toGregorianCalendar().getTime();

        log.info("retrieved {} events ", trafficEventList.size()) ;

        return trafficEventList;

Next we will retrieve the data out of the XML and split it out into something more event like. For every sensor point per vehicle we will extract one TrafficEvent.

    public class TrafficEvent {

        private VehicleClass vehicleClass;
        private Integer trafficIntensity;

        private Integer vehicleSpeedCalculated;

        private Integer vehicleSpeedHarmonical;

        private String sensorId;
        private String sensorDescriptiveId;

        private Integer lveNumber;
        private Date timeRegistration;
        private Date lastUpdated;

        actueel_publicatie: 1 = data is less then 3 minutes old.
        private Boolean recentData;

        Indicate if the sensor (meetPunt) was available when trying to retrieve the data
        private Boolean available;

        private Integer sensorDefect;
        private Integer sensorValid;


The VehicleClass is just an enum with the vehicle type.


We will also retrieve the detailed sensor information from the XML containing the sensor descriptions.

    public class SensorData {

        private String uniekeId;

        private Integer sensorId;
        Meetpunt beschrijvende Id
        private String sensorDescriptiveId;

        private String name;

        Unique road number.
            More info in the dataset of numbered roads in the "Wegenregister" (Roads registry), field: locatieide,
            Or the dataset "De beheersegmenten van de genummerde wegen" by AWV, field ident8,
        private String ident8;

        Reference to the lane of the measurement point.
          The character indicates the lane type.
            R: Regular lane
            B: Bus lane or similar
            TR: measurement of the traffic in the opposite direction (p.e. in or near tunnels) on the corresponding R-lane.
            P: Hard shoulder lane
            W: parking or other road
            S: Lane for hard shoulder running
            A: Hatched area

          Counting starts at R10 for the first regular lane of the main road. Lane numbers increase from right/slower to left/faster lanes.
          Lanes 09, 08, 07, ... are positioned right of this first lane, and mainly indicate access/merging lanes, deceleration lanes, recently added lanes, lanes for hard shoulder running, bus lanes
          Lanes 11, 12, 13, ... are positioned left of lane R10.
          The lane number 00 is used for measurement points on the hard shoulder (P00).
          The TR-lane is identical to the corresponding R-lane (TR10=R10,TR11=R11,TR12=R12,...), but returns the data of the "ghost traffic" instead.
          (The data for TR10 and R10 are provided by the same detection loops.)
        private String trafficLane;

Write these events to a topic.

    public void sendMessage(TrafficEvent trafficEvent) {

        log.info("Send message to the trafficEventOutput channel");

    public void sendSensorData(SensorData sensorData) {

The events will be sent to Kafka as JSON messages.

With the @Scheduled annotation Spring Boot will read in the events every 60 seconds.

    @Scheduled(fixedRate = 60000)
    public void run() throws Exception {

When you are taking your data in, it is important to decide what you want to send in.

You do not want to remove too much information nor do you want the events becoming too bloated. Meaning, that they contain too much information and you needing to spend a lot of time extracting information when analysing your data. Keep them as close to the actual event as possible, only adding in data if this is required.

In our current example the sensor location does not need to be part of the traffic events as it is pretty static. If in your situation, you have another data entry where your sensor specific data changes every few events, it might be worthwhile to add it to your event when taking it in. So that later on you do not have to spend time joining that data together.

Sometimes your intake data is also too large, it is not wrong to ignore certain properties when taking in data in your stream.

In our case we ignore a lot of the properties within the XML, as they do not serve our example. Having less properties to analyze can make your life easier, but if that raw data is no longer available you have lost that information for good.

Be wise with what you remove as time travel is not something we can code in, ignored data is lost forever.


  • Think in events
  • Keep the data structure as flat as possible
  • Do not optimize your data too soon

Native Java Stream Processing

Do not forget

Do not forget that you can also process your events in native Java. You will not have a lot of fancy features available but it might get the job done.

Especially when you take into consideration the extra cost involved in introducing a streaming framework. For both Kafka and Storm you not only need to set up a cluster of the framework itself, but also of Zookeeper.

That setup does not come for free and will need to be maintained in the future.

Easy to get started

With Spring Cloud Stream it is easy to start processing your stream of data in native Java.

First define a SubscribableChannel.

    SubscribableChannel trafficEvents();

Then you will need to define a MessageHandler which will describe what you will do with every message you process.

    MessageHandler messageHandler = (message -> {
            log.info("retrieved message with header " + message.getHeaders().toString());
            log.info("retrieved message " + message.getPayload().toString());

            TrafficEvent event = (TrafficEvent) message.getPayload();

            log.info(" the sensor id is " + event.getSensorId());

            if (event.getTrafficIntensity() > 0) {
                log.info("We now have {} vehicles on the road {}", event.getTrafficIntensity(), event.getSensorId());

                int vehicleCountForEvent = event.getTrafficIntensity();

                if (vehicleCount.get(event.getSensorId()) != null) {
                    vehicleCountForEvent += vehicleCount.get(event.getSensorId());

                log.info("We now had total: {} vehicles", vehicleCountForEvent);

                vehicleCount.put(event.getSensorId(), vehicleCountForEvent);

            if (event.getVehicleSpeedCalculated() > 0) {
                if (lowestWithTraffic.get(event.getSensorId()) == null || lowestWithTraffic.get(event.getSensorId()).getVehicleSpeedCalculated() > event.getVehicleSpeedCalculated()) {
                    lowestWithTraffic.put(event.getSensorId(), event);

                if (highestWithTraffic.get(event.getSensorId()) == null || highestWithTraffic.get(event.getSensorId()).getVehicleSpeedCalculated() < event.getVehicleSpeedCalculated()) {
                    highestWithTraffic.put(event.getSensorId(), event);


Finally, link that MessageHandler to an InputChannel.


There you go, you are now processing your stream of data in native Java.

It does become obvious that doing something more fancy, like windowing and aggregation, will require you to write all of that logic yourself. This can get out of hand pretty quickly, so do watch out for that.

But for simple data processing, nothing beats some native Java.

Takeaways Native Java

  • Can easily handle 1000 events per second
  • Easy to get started
  • You will lack advanced features like windowing, aggregation, …

Kafka Streams with Spring Kafka


Spring Kafka allows us to easily make use of Apache Kafka.

Kafka is designed to handle large streams of data. Messages are published into topics and can be stored for mere minutes or indefinitely. It is highly scalable allowing topics to be distributed over multiple brokers.

Kafka Streams allows us to write stream processing applications within the Kafka cluster itself.

For this reason, Kafka Streams will use topics for both input and output allowing it to store intermediate results within Kafka itself.

What “topics” does Kafka Streams use


A KStream records a stream of key/value pairs and can be defined from one or more topics. It does not matter if a key exists multiple times within the KStream, when you read in the data of a KStream every record will be sent to you.


A KTable is a changelog stream of a primary keyed table, meaning that whenever a key exists multiple times within the KTable you will receive only the most recent record.


Like a KTable, but it is replicated over all Kafka Streams instances, so do be careful.


This is an intermediate format based on a regrouped stream of records based on a KStream, with usually, a different key than the original primary key. It is derived from a groupBy() or a groupByKey() on a KStream. Via aggregate(), count() or reduce() it can be converted to a KTable.


This is pretty similar to a KGroupedStream, but a KGroupedTable is derived from a KTable via groupBy(). It can be reconverted to a KTable via aggregate(), count() or reduce().

Coding with Spring Kafka

We still have the Spring Cloud Stream topics to which we send in some data. Let’s use these but now using Kafka.

First we are going to take in the static data of the sensors into a KTable.

    KStream<String, SensorData> sensorDescriptionsStream =
        streamsBuilder.stream("sensorDataOutput", Consumed.with(Serdes.String(), new SensorDataSerde()));

    KStream<String, SensorData> sensorDescriptionsWithKey =
        sensorDescriptionsStream.selectKey((key, value) -> value.getUniekeId());

    KTable<String, SensorData> sensorDataKTable =
        streamsBuilder.table("dummy-topic", Consumed.with(Serdes.String(), new SensorDataSerde()));

The main reason we are using a KTable is that it makes it easy to be sure to only get the most recent state of that sensor, as a KTable will only return one result per key. dummy-topic is just the name I chose. For my example it is not that important to have a well defined topic name. But do realize that Kafka Streams will persist the state of a Ktable within Kafka topics.

Subsequently we are going to enrich the traffic event with the sensor data.

    KStream<String, TrafficEvent> stream =
            streamsBuilder.stream("trafficEventsOutput", Consumed.with(Serdes.String()
                    , new TrafficEventSerde()));
    stream.selectKey((key,value) -> value.getSensorId())
            .join(sensorDataKTable,((TrafficEvent trafficEvent, SensorData sensorData) -> {
                return trafficEvent;
            }), Joined.with(Serdes.String(), new TrafficEventSerde(), null))

Resulting in a new KStream with enriched TrafficEvents.

The .stream(String topic, Consumed<K,V> consumed) will consume all entries from a topic and transform these into a stream. Mapping these to topic records with a key and a value. In our case the key is just a string, while the body of the topic will be a JSON message which gets converted into a TrafficEvent.

With join(), full definition:

    <VT, VR> KStream<K, VR> join(final KTable<K, VT> table,
         final ValueJoiner<? super V, ? super VT, ? extends VR> joiner,
         final Joined<K, V, VT> joined);

We join our KTable with our TrafficEvent records using the ValueJoiner we pass along which will result in a new Joined result. The ValueJoiner is just a function in which we indicate what needs to be done with both records the function receives. In our case a TrafficEvent and a SensorData. The Joined describes the new record structure we will write towards Kafka using .to(String topic) sending the newly generated records to that Kafka topic.

Once this stream has started, it will continue processing these events whenever a new record is inserted into the intake topic.

For some of our further processing we do not care for all traffic events, so let’s filter out some.

    KStream<String, TrafficEvent> streamToProcessData = 
        streamsBuilder.stream("enriched-trafficEventsOutput", Consumed.with(Serdes.String(), new TrafficEventSerde()));

    streamToProcessData.selectKey((key,value) -> value.getSensorId())
        .filter((key, value) -> canProcessSensor(key));

Filtering happens on the key of the records, so first we will use selectKey() passing along a KeyMapper to map to the new key. The KeyMapper is a function to which you pass along the field which you want to become the new key.

    private boolean canProcessSensor(String key) {
        return this.sensorIdsToProcess.contains(key);

Then we will use filter() to filter out the keys we want to retain which match the given Predicate. In our case the predicate just verifies if a key appears within a List:

For every record we will now do some simple processing with updateStats():

        .selectKey((key,value) -> value.getSensorId())
        .filter((key, value) -> canProcessSensor(key))
        .foreach((key, value) -> updateStats(value));

The updateStats() method just updates some basic counters to track how much traffic has been processed since we started with the data intake to a hashtable. So that we know how many vehicles have passed, the highest speed detected, …


In an ideal world all events arrive in a perfect and timely fashion within our Kafka system.

In an ideal world we can also process all the events we want to process.

In the real world however, this does not compute. Events tend to arrive out of order and too late.

If you want to get a count of all the vehicles which ran over your road network from 21:00 to 21:05 but one of your sensors sends its events too late, the count you have generated will not be correct.

Windowing allows you to mitigate these risk by

  • Limiting the scope of your stream processing
  • Allowing you to catch some “late” events within a window

For adding windows you use .windowedBy, in this example we define a window of 5 minutes which gets every 10 minutes. Then you will need to aggregate the results per window with .aggregate.

Do not forget to provide the correct Materialized parameters so Kafka knows what type of key and value is used as input by the aggregation.

    private void createWindowStreamForAverageSpeedPerSensor(KStream<String, TrafficEvent> streamToProcessData) {
        Initializer initializer = () -> new SensorCount();

            .aggregate(initializer, (key, value, aggregate) -> aggregate.addValue(value.getVehicleSpeedCalculated()),
                    Materialized.with(Serdes.String(), new JsonSerde<>(SensorCount.class)))
                    .mapValues(SensorCount::average, Materialized.with(new WindowedSerde<>(Serdes.String()), Serdes.Double()))
                    .map(((key, average) -> new KeyValue<>(key.key(), average)))
                    .through("average-speed-per-sensor", Produced.with(Serdes.String(), Serdes.Double()))
                    .foreach((key, average) -> log.info((String.format(" =======> average speed for the sensor %s is now %s", key, average))));
    streamToProcessData.filter((key, value) -> canProcessSensor(key))
                .selectKey((key,value) -> value.getSensorData().getName().replaceAll("\\s","").replaceAll("-", ""))

    KStream<String, TrafficEvent> streamPerHighwayLaneToProcess = 
            streamsBuilder.stream("traffic-per-lane", Consumed.with(Serdes.String(), new TrafficEventSerde()));


Takeaways Kafka Streams and Spring Kafka

  • When you have a Kafka cluster lying around, using Kafka Streams is a no-brainer
  • Excellent support within Spring
  • Easy to get started
  • Using the Kafka Streams DSL feels quite natural

Apache Storm


It was first created at Twitter who open sourced it as an Apache Project. One of the first streaming frameworks that got widely adopted.

Spouts & Bolts

When you work with Storm you need to think in Spouts, Bolts and Tuples.

A Spout is the origin of your streams. It will read in Tuples from an external source and can be either reliable or unreliable.

Reliable just means that when something goes wrong within your stream processing, the spout can replay the Tuple. While an unreliable spout will go for the good old fire-and-forget approach.

Spouts can also emmit to more than one stream.

Spouts will generate Tuples, the main data structure within Storm. A Tuple is a named list of values, where a value can be of any type. It is however important that Storm will serialize all the values within a Tuple, so for a more exotic type you will need to implement a serializer yourself.

Bolts do all the processing of your streams. A Bolt can send out to more then 1 stream. It is also possible to define a Stream Grouping on your Bolts allowing you to tailor the distribution of your workload over the various Bolts of your Storm topology.

Multiple instances of a Bolt will run as tasks.

You have the following Stream Groupings:

  • Shuffle Grouping: completely random
  • Fields Grouping: based on the value of certain fields, Storm will make sure that all the Tuples with the same “key” will be processed by the same Bolt, handy for word counts for example - great business value
  • Partial Key Grouping: pretty similar to fields grouping, but with some extra load balancing
  • All grouping: the entire stream will go to all the tasks of a Bolt, use this with care
  • None Grouping: implies that you don’t care how it gets processed - which corresponds with a shuffle grouping
  • Direct Grouping: here the producer of the Tuple will decide which task of the Bolt will receive the Tuple for processing
  • Local or Shuffle Grouping: this will also take a look at the worker processes running the Bolt’s tasks, this in order to make the flow somewhat more efficient.

Now let’s get started with some code.

First take in some necessary dependencies:



The idea is to get to a Storm topology with one Spout and two Bolts.

    final TopologyBuilder tp = new TopologyBuilder();
        tp.setSpout("kafka_spout", new KafkaSpout<>(spoutConfig), 1).setDebug(false);
        tp.setBolt("trafficEvent_Bolt", new TrafficEventBolt(sensorIdsToProcess)).setDebug(false)
        tp.setBolt("updateTrafficEventStats_bolt", new TrafficCountBolt()).setDebug(true)
                .fieldsGrouping("trafficEvent_Bolt", new Fields("sensorId"));
        return tp.createTopology();

First we will define a KafkaSpout which will take in the data of a Kafka topic.

    protected KafkaSpoutConfig<String, String> getKafkaSpoutConfig(String bootstrapServers) {
        ByTopicRecordTranslator<String, String> trans = new ByTopicRecordTranslator<>(
                (r) -> new Values(r.topic(), r.partition(), r.offset(), r.key(), r.value()),
                new Fields("topic", "partition", "offset", "key", "value"));
                (r) -> new Values(r.topic(), r.partition(), r.offset(), r.key(), r.value()),
                new Fields("topic", "partition", "offset", "key", "value"));
        return KafkaSpoutConfig.builder(bootstrapServers, new String[]{"trafficEventsOutput"})
                .setProp(ConsumerConfig.GROUP_ID_CONFIG, "kafkaSpoutTestGroup")

For completeness this is the retryService which just handles some retrying whenever your Kafka cluster is behaving naughty:

    protected KafkaSpoutRetryService getRetryService() {
            return new KafkaSpoutRetryExponentialBackoff(KafkaSpoutRetryExponentialBackoff.TimeInterval.microSeconds(500),
                    KafkaSpoutRetryExponentialBackoff.TimeInterval.milliSeconds(2), Integer.MAX_VALUE, KafkaSpoutRetryExponentialBackoff.TimeInterval.seconds(10));

Then we will emmit that data to a TrafficEventBolt which will filter out the events we want to process further.

    public class TrafficEventBolt extends BaseRichBolt {
        private OutputCollector collector;

        private final List<String> sensorIds;

        public TrafficEventBolt(List<String> sensorIdsToProcess) {
            this.sensorIds = sensorIdsToProcess;

        public void prepare(Map map, TopologyContext topologyContext, OutputCollector outputCollector) {
            this.collector = outputCollector;

        public void execute(Tuple input) {
            log.info("input = [" + input + "]");


            TrafficEvent trafficEvent = new Gson().fromJson((String)input.getValueByField("value"), TrafficEvent.class);

            if (sensorIds.contains(trafficEvent.getSensorId())) {
                collector.emit(input, new Values(trafficEvent.getSensorId(), trafficEvent.getVehicleSpeedCalculated(), trafficEvent.getTrafficIntensity()));
            } else {


        public void declareOutputFields(OutputFieldsDeclarer declarer) {
            declarer.declare(new Fields("sensorId", "speed", "trafficIntensity"));

Finally we will send out the tuples to a TrafficCountBolt which will gather some general statistics.

    public class TrafficCountBolt extends BaseRichBolt {
        private OutputCollector collector;

        private final HashMap<String, Integer> countPerSensors = new HashMap<>();

        public void prepare(Map map, TopologyContext topologyContext, OutputCollector outputCollector) {
            this.collector = outputCollector;

        public void execute(Tuple input) {
            log.info("input = [" + input + "]");

            Integer count = countPerSensors.get((String)input.getValueByField("sensorId"));
            if (count == null) {
                count = 0;
            count = count+ (Integer) input.getValueByField("trafficIntensity");
            countPerSensors.put(input.getString(0), count);

            collector.emit(new Values(input.getString(0), count));


        public void declareOutputFields(OutputFieldsDeclarer declarer) {
            declarer.declare(new Fields("sensorId", "count"));


Storm also knows about the concept of windowing.

    public class CountPerSensorIdBolt extends BaseWindowedBolt {
        private OutputCollector collector;

        private final HashMap<String, Integer> countPerSensors = new HashMap<>();

        public void execute(TupleWindow tupleWindow) {

            for (Tuple input : tupleWindow.get()) {
                Integer count = countPerSensors.get((String)input.getValueByField("sensorId"));
                if (count == null) {
                    count = 0;
                count = count+ (Integer) input.getValueByField("trafficIntensity");
                countPerSensors.put(input.getString(0), count);

                collector.emit(new Values(input.getString(0), count));


Subsequently you can define this bolt within a topology at which moment you will also define the size or duration of the window:

In this example we are just using windows with a fixed duration of five seconds.

    private StormTopology getTopologyKafkaSpout(KafkaSpoutConfig<String, String> spoutConfig) {
        final TopologyBuilder tp = new TopologyBuilder();
        tp.setSpout("kafka_spout", new KafkaSpout<>(spoutConfig), 1).setDebug(false);
        tp.setBolt("trafficEvent_Bolt", new TrafficEventBolt(sensorIdsToProcess)).setDebug(false)
        tp.setBolt("updateTrafficEventStats_bolt", new TrafficCountBolt()).setDebug(true)
                .fieldsGrouping("trafficEvent_Bolt", new Fields("sensorId"));
        tp.setBolt("windowedProcessBolt", new CountPerSensorIdBolt().withWindow(BaseWindowedBolt.Duration.seconds(5)))
        return tp.createTopology();

You can also pass in an extra parameter slidingInterval to define a sliding window.

    withWindow(Duration windowLength, Duration slidingInterval)

Both the windowLength and the slidingInterval can also be represented by a Count, which will base the window duration on the amount of tuples being processed. Either determining the length of the window by the tuples, or when to slide.

    withWindow(Count windowLength, Duration slidingInterval)

    withWindow(Duration windowLength, Count slidingInterval)

Even tumbling windows are possible:

    withTumblingWindow(BaseWindowedBolt.Count count)

    withTumblingWindow(BaseWindowedBolt.Duration duration)

Please note that a tuple belongs to only one of the tumbling windows, while with a sliding window it is very much possible that a single tuple is processed within multiple windows.

Stream API

The Storm Streams API is pretty new.

It tends to provide a DSL which corresponds more with other streaming DSLs, making your data processing feel more natural and less clunky, as compared to be thinking in spouts and bolts.

In the background it will convert the DSL to spouts and bolts though, so knowing how Storm works internally is still pretty important.

Takeaways Apache Storm

  • It is pretty mature
  • Low latency / high throughput
  • It does tend to feel pretty clunky thinking in Spouts and Bolts - for a developer it is not that big of a hassle, but for a data scientist I can imagine that at times it will be harder to wrap your head around it


In order to get started with basic stream processing you do not need Kafka or Apache Storm, native Java is good enough for you to take your very first steps when processing a stream of data. It is easy, everybody understands it and you will have less moving parts within your software landscape which can cause issues.

Using a dedicated streaming platform will become necessary when you want to do more advanced streaming operations or when performance becomes more and more important. The existing platforms can easily scale up to the processing of thousands of messages a second, something which is going to be much harder to achieve when building your solution yourself.

Do not make the mistake of re-inventing the wheel by writing your own streaming platform, others have done that hard work for you.

Kafka Streams is a no-brainer to use when you have a Kafka cluster lying around, stream processing there feels natural and it is easy to get going. If however you do not have a Kafka cluster available, it will come with an extra cost of setting it up and maintaining it. There do exist managed solutions in order to make your life easier.

Apache Storm is a pretty robust framework which has been around for some time and is used by many. However, writing the processing logic feels quite clunky and I can imagine that for a non-developer it also might feel quite unnatural. They are currently working on a new streaming API which should alleviate that issue though. According to their GitHub, a release of version 2.0 has already happened, but their website does not reflect it yet.

When doing stream processing always think about how messages will be handled as most streaming or messaging platforms use an at-least-once approach, meaning that the same message can be processed more than once by the streaming pipeline. Both Kafka Streams and Apache Storm can be configured to provide exactly-once processing within their streaming pipelines. For Kafka Streams it means using Kafka transactions while for Storm this can be achieved by Trident. Even then, it is only within the streaming pipeline itself meaning that as soon as your processed results leave the streaming platform, you will be back to at-least-once guarantees.

Tom is a Senior Developer at Ordina Belgium, passionate about all software related to data. As competence leader Big & Fast Data he guides his fellow developers through dark data swamps by giving workshops and presentations. Tom is passionate about learning new technologies and frameworks.