Hello everybody! First of all, thanks for reading :D
I am currently working on my bachelor's final project which is a
comparison between Spark Streaming and Flink. Now let's focus on the
problem:
- THE PROBLEM: the problem is that my program is writing to Kafka more
than once every window (is creating 2-3 or more lines per window,
meanwhile it is supposed to create 1 line per window as with the reduce
function it lets only one element). I have the same code written in
Spark and it works perfectly. I have been trying to find info about this
issue and I haven't found anything :(. Also I have been trying changing
some functions' parallelism and some more things and nothing worked, and
I can not realise where can be the problem.
- MY CLUSTER: I am using Flink 1.2.0 and OpenJDK 8. I have 3 computers:
1 JobManager, 2 TaskManagers (4 cores, 2GB RAM, 4 task slots each
TaskManager).
- INPUT DATA: lines produced by one java producer to the Kafka 24
partitions' topic with two elements: incremental value and creation
timestamp:
1 1497790546981
2 1497790546982
3 1497790546983
4 1497790546984
..................
- MY JAVA APPLICATION:
+ It reads from a Kafka topic with 24 partitions (Kafka is in the same
machine than the JobManager).
+ The filter functions are useless together with the union as I use them
just for checking their latency.
+ Basically, it adds a "1" to each line,then there is a tumbling window
every 2 seconds, and the reduce function sum all this 1's and all the
timestamps, this last timestamp is later divided in the map function
between the sum of 1's which gives me the average, and finally in the
last map function it adds a timestamp of the current moment to each
reduced line and the difference between this timestamp and the average
timestamp.
+ This line is written to Kafka (to a 2 partitions' topic).
######################### - CODE - ####################################
//FLINK CONFIGURATION
final StreamExecutionEnvironment env = StreamExecutionEnvironment
.getExecutionEnvironment();
env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime);
//env.setParallelism(2);
//KAFKA CONSUMER CONFIGURATION
Properties properties = new Properties();
properties.setProperty("bootstrap.servers", "192.168.0.155:9092");
FlinkKafkaConsumer010<String> myConsumer = new
FlinkKafkaConsumer010<>(args[0], new SimpleStringSchema(), properties);
//KAFKA PRODUCER
Properties producerConfig = new Properties();
producerConfig.setProperty("bootstrap.servers",
"192.168.0.155:9092");
producerConfig.setProperty("acks", "0");
producerConfig.setProperty("linger.ms", "0");
//MAIN PROGRAM
//Read from Kafka
DataStream<String> line = env.addSource(myConsumer);
//Add 1 to each line
DataStream<Tuple2<String, Integer>> line_Num = line.map(new
NumberAdder());
//Filted Odd numbers
DataStream<Tuple2<String, Integer>> line_Num_Odd =
line_Num.filter(new FilterOdd());
//Filter Even numbers
DataStream<Tuple2<String, Integer>> line_Num_Even =
line_Num.filter(new FilterEven());
//Join Even and Odd
DataStream<Tuple2<String, Integer>> line_Num_U =
line_Num_Odd.union(line_Num_Even);
//Tumbling windows every 2 seconds
AllWindowedStream<Tuple2<String, Integer>, TimeWindow>
windowedLine_Num_U = line_Num_U
.windowAll(TumblingProcessingTimeWindows.of(Time.seconds(2)));
//Reduce to one line with the sum
DataStream<Tuple2<String, Integer>> wL_Num_U_Reduced =
windowedLine_Num_U.reduce(new Reducer());
//Calculate the average of the elements summed
DataStream<String> wL_Average = wL_Num_U_Reduced.map(new
AverageCalculator());
//Add timestamp and calculate the difference with the average
DataStream<String> averageTS = wL_Average.map(new TimestampAdder());
//Send the result to Kafka
FlinkKafkaProducer010Configuration<String> myProducerConfig =
(FlinkKafkaProducer010Configuration<String>) FlinkKafkaProducer010
.writeToKafkaWithTimestamps(averageTS, "testRes", new
SimpleStringSchema(), producerConfig);
myProducerConfig.setWriteTimestampToKafka(true);
env.execute("TimestampLongKafka");
}
//Functions used in the program implementation:
public static class FilterOdd implements FilterFunction<Tuple2<String,
Integer>> {
private static final long serialVersionUID = 1L;
public boolean filter(Tuple2<String, Integer> line) throws Exception
{
Boolean isOdd = (Long.valueOf(line._1.split(" ")[0]) % 2) != 0;
return isOdd;
}
};
public static class FilterEven implements FilterFunction<Tuple2<String,
Integer>> {
private static final long serialVersionUID = 1L;
public boolean filter(Tuple2<String, Integer> line) throws Exception
{
Boolean isEven = (Long.valueOf(line._1.split(" ")[0]) % 2) == 0;
return isEven;
}
};
public static class NumberAdder implements MapFunction<String,
Tuple2<String, Integer>> {
private static final long serialVersionUID = 1L;
public Tuple2<String, Integer> map(String line) {
Tuple2<String, Integer> newLine = new Tuple2<String,
Integer>(line, 1);
return newLine;
}
};
public static class Reducer implements ReduceFunction<Tuple2<String,
Integer>> {
private static final long serialVersionUID = 1L;
public Tuple2<String, Integer> reduce(Tuple2<String, Integer> line1,
Tuple2<String, Integer> line2) throws Exception {
Long sum = Long.valueOf(line1._1.split(" ")[0]) +
Long.valueOf(line2._1.split(" ")[0]);
Long sumTS = Long.valueOf(line1._1.split(" ")[1]) +
Long.valueOf(line2._1.split(" ")[1]);
Tuple2<String, Integer> newLine = new Tuple2<String,
Integer>(String.valueOf(sum) + " " + String.valueOf(sumTS),
line1._2 + line2._2);
return newLine;
}
};
public static class AverageCalculator implements
MapFunction<Tuple2<String, Integer>, String> {
private static final long serialVersionUID = 1L;
public String map(Tuple2<String, Integer> line) throws Exception {
Long average = Long.valueOf(line._1.split(" ")[1]) / line._2;
String result = String.valueOf(line._2) + " " +
String.valueOf(average);
return result;
}
};
public static final class TimestampAdder implements MapFunction<String,
String> {
private static final long serialVersionUID = 1L;
public String map(String line) throws Exception {
Long currentTime = System.currentTimeMillis();
String totalTime = String.valueOf(currentTime -
Long.valueOf(line.split(" ")[1]));
String newLine = line.concat(" " + String.valueOf(currentTime) +
" " + totalTime);
return newLine;
}
};
######################### - CODE - ####################################
- SOME OUTPUT DATA: this output has been written to the 2 partitions'
topic, and with a producing rate of less than 1000 records/second (**in
this case it is creating 3 output lines per window):
(check that every 3 lines correspond to the same 2 seconds part)
1969 1497791240910 1497791241999 1089 1497791242001 1091
1973 1497791240971 1497791241999 1028 1497791242002 1031
1970 1497791240937 1497791242094 1157 1497791242198 1261
1917 1497791242912 1497791243999 1087 1497791244051 1139
1905 1497791242971 1497791243999 1028 1497791244051 1080
1916 1497791242939 1497791244096 1157 1497791244199 1260
1994 1497791244915 1497791245999 1084 1497791246002 1087
1993 1497791244966 1497791245999 1033 1497791246004 1038
1990 1497791244939 1497791246097 1158 1497791246201 1262
Thanks again in advance!
---
FRANCISCO BORJA ROBLES MARTÍN
Escuela Técnica Superior de Ingeniería Informática
Campus Montegancedo s/n
Universidad Politécnica de Madrid (Technical University of Madrid)
