Apache Flink : Window Function on AllWindowed Stream - Combining Kafka Topics - json

Question

I am trying to combine two kafka topics using the a single kafka consumer on a list of topics, further convert the json string in the stream to POJO. Then, join them via keyBy ( On event time field ) and to merge them as a single fat json, I was planning to use a window stream and apply a window function on the window stream. The assumption is that Topic-A & Topic-B can be joined on Event Time and only one pair ( Topic A ( JSON ) , Topic B (JSON ) will be present with the same eventTime. Hence was planning to use a coutWindow(2) post keyBy on eventTime.
I have couple of questions for the same;
Is the approach fine for merging topics and creating a single JSON?
The window function on All Window stream doesnt seem to work fine; Any pointers will be greatly appreciated.
Code Snippet :
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
logger.info("Flink Stream Window Charger has started");
Properties properties = new Properties();
properties.setProperty("bootstrap.servers", "127.0.0.1:1030");
properties.setProperty("zookeeper.connect", "127.0.0.1:2181/service-kafka");
properties.setProperty("group.id", "group-0011");
properties.setProperty("auto.offset.reset", "smallest");
List < String > names = new ArrayList < > ();
names.add("Topic-A");
names.add("Topic-B");
DataStream < String > stream = env.addSource(new FlinkKafkaConsumer08 < > (names, new SimpleStringSchema(), properties));
DataStream < TopicPojo > pojo = stream.map(new Deserializer()).keyBy((eventTime) -> TopicPojo.getEventTime());
List < String > where = new ArrayList < String > ();
AllWindowedStream < String, GlobalWindow > data_window = pojo.flatMap(new Tokenizer()).countWindowAll(2);
DataStream < String > data_charging = data_window.apply(new MyWindowFunction());
data_charging.addSink(new SinkFunction < String > () {
public void invoke(String value) throws Exception {
// Yet to be implemented - Merge two POJO into one
}
});
try
{
env.execute();
} catch (Exception e)
{
return;
}
}
}
class Tokenizer implements FlatMapFunction < TopicPojo, String > {
private static final long serialVersionUID = 1 L;
#Override
public void flatMap(TopicPojo value, Collector < String > out) throws Exception {
ObjectMapper mapper = new ObjectMapper();
out.collect(mapper.writeValueAsString(value));
}
}
class MyWindowFunction implements WindowFunction < TopicPojo, String, String, GlobalWindow > {
#Override
public void apply(String key, GlobalWindow window, Iterable < TopicPojo > arg2, Collector < String > out)
throws Exception {
int count = 0;
for (TopicPojo in : arg2) {
count++;
}
// Test Result - TO be modified
out.collect("Window: " + window + "count: " + count);
}
}
class Deserializer implements MapFunction < String, TopicPojo > {
private static final long serialVersionUID = 1 L;
#Override
public TopicPojo map(String value) throws IOException {
// TODO Auto-generated method stub
ObjectMapper mapper = new ObjectMapper();
TopicPojo obj = null;
try {
System.out.println(value);
obj = mapper.readValue(value, TopicPojo.class);
} catch (JsonParseException e) {
// TODO Auto-generated catch block
throw new IOException("Failed to deserialize JSON object.");
} catch (JsonMappingException e) {
// TODO Auto-generated catch block
throw new IOException("Failed to deserialize JSON object.");
} catch (IOException e) {
// TODO Auto-generated catch block
throw new IOException("Failed to deserialize JSON object.");
}
return obj;
}
}
I am getting -
The method apply(AllWindowFunction) in the type AllWindowedStream is not applicable for the arguments (MyWindowFunction) error.

Answer 1

An AllWindowedStream is a non-keyed stream, and so the apply method for AllWindowedStreams doesn't have a key parameter. Since you are windowing a keyed stream, your data_window should be a KeyedStream.