I've develop a Flink program that reads tweets from Twitter and push them on Kafka. Then it get back the tweets from Kafka and process them.
The "Tweets processing" transformation extracts hashtags and users from the text of the tweet and emit them in the default output and every pair of them in a side output.
The attached image is picked from the Flink Web UI. I don't understand why the Kafka Source and the Tweets processing operator are merged into a single task and primarily I want that the Tweets sink receive all the raw tweets from the Kafka Source not the output of the Tweets processing operator.
Is the program correct?
Datalow
This the relevant part of the code:
FlinkKafkaConsumer010<String> myConsumer = new FlinkKafkaConsumer010<String>(Constants.KAFKA_TWEETS_TOPIC, new SimpleStringSchema(), properties);
myConsumer.setStartFromLatest();
DataStream<String> tweetsStream = env
.addSource(myConsumer)
.name("Kafka tweets consumer");
SingleOutputStreamOperator<List<String>> tweetsAggregator = tweetsStream
.timeWindowAll(Time.seconds(7))
.aggregate(new StringAggregatorFunction())
.name("Tweets aggregation");
DataStreamSink tweetsSink = tweetsAggregator.addSink(new TweetsSink())
.name("Tweets sink")
.setParallelism(1);
SingleOutputStreamOperator<String> termsStream = tweetsStream
// extracting terms from tweets
.process(new TweetParse())
.name("Tweets processing");
DataStream<Tuple2<String, Integer>> counts = termsStream
.map(new ToTuple())
// Counting terms
.keyBy(0)
.timeWindow(Time.seconds(13))
.sum(1)
.name("Terms processing");
DataStream<Tuple3<String, String, Integer>> edgesStream = termsStream.getSideOutput(TweetParse.outputTag)
.map(new ToTuple3())
// Counting terms pairs
.keyBy(0, 1)
.timeWindow(Time.seconds(19))
.sum(2)
.name("Edges processing");
You are creating two different dataflow with tweetsStream. the first is tweetsAggregator and the second is termsStream. Then you are creating two different dataflow from termsStream again: counts and edgesStream. The Sink operators has no output. So it cannot generate data to another operator and it must be the last operator to use. You have to start with a data Source operator addSource(myConsumer), chain as much as transformation you want timeWindowAll, aggregate, map, keyBy, and then call a sink operator. You can call more than one sink if you want, but remember that sinks don't generate data stream to other operators, they are consumers.
Related
In the following code
public SingleOutputStreamOperator<T> assignTimestampsAndWatermarks(
WatermarkStrategy<T> watermarkStrategy) {
final WatermarkStrategy<T> cleanedStrategy = clean(watermarkStrategy);
// match parallelism to input, to have a 1:1 source -> timestamps/watermarks relationship
// and chain
final int inputParallelism = getTransformation().getParallelism();
final TimestampsAndWatermarksTransformation<T> transformation =
new TimestampsAndWatermarksTransformation<>(
"Timestamps/Watermarks",
inputParallelism,
getTransformation(),
cleanedStrategy);
getExecutionEnvironment().addOperator(transformation);
return new SingleOutputStreamOperator<>(getExecutionEnvironment(), transformation);
}
The assignTimestampsAndWatermarks() receives the main stream and assigns timestamps and watermarks based on the strategy specified in params, at the end, it will return SingleOutputStreamOperator which is the updated stream with timestamps and watermarks generated.
My question is, what TimestampsAndWatermarksTransformation does here (internally) and what is the effect of this line getExecutionEnvironment().addOperator(transformation); as well.
When you call assignTimestampsAndWatermarks on a stream, this code adds an operator to the job graph to do the timestamp extraction and watermark generation. This is wiring things up so that the specified watermarking will actually get done.
Internally there are two types of Transformation: (1) physical transformations, such as map or assignTimestampsAndWatermarks, which alter the stream records, and (2) logical transformations, such as union, that only affect the topology.
I have a simple Flink stream processing application (Flink version 1.13). The Flink app reads from Kakfa, does stateful processing of the record, then writes the result back to Kafka.
After reading from Kafka topic, I choose to use reinterpretAsKeyedStream() and not keyBy() to avoid a shuffle, since the records are already partitioned in Kakfa. The key used to partition in Kakfa is a String field of the record (using the default kafka partitioner). The Kafka topic has 24 partitions.
The mapping class is defined as follows. It keeps track of the state of the record.
public class EnvelopeMapper extends
KeyedProcessFunction<String, Envelope, Envelope> {
...
}
The processing of the record is as follows:
DataStream<Envelope> messageStream =
env.addSource(kafkaSource)
DataStreamUtils.reinterpretAsKeyedStream(messageStream, Envelope::getId)
.process(new EnvelopeMapper(parameters))
.addSink(kafkaSink);
With parallelism of 1, the code runs fine. With parallelism greater than 1 (e.g. 4), I am running into the follow error:
2022-06-12 21:06:30,720 INFO org.apache.flink.runtime.executiongraph.ExecutionGraph [] - Source: Custom Source -> Map -> Flat Map -> KeyedProcess -> Map -> Sink: Unnamed (4/4) (7ca12ec043a45e1436f45d4b20976bd7) switched from RUNNING to FAILED on 100.101.231.222:44685-bd10d5 # 100.101.231.222 (dataPort=37839).
java.lang.IllegalArgumentException: KeyGroupRange{startKeyGroup=96, endKeyGroup=127} does not contain key group 85
Based on the stack trace, it seems the exception happens when EnvelopeMapper class validates the record is sent to the right replica of the mapper object.
When reinterpretAsKeyedStream() is used, how are the records distributed among the different replicas of the EventMapper?
Thank you in advance,
Ahmed.
Update
After feedback from #David Anderson, replaced reinterpretAsKeyedStream() with keyBy(). The processing of the record is now as follows:
DataStream<Envelope> messageStream =
env.addSource(kafkaSource) // Line x
.map(statelessMapper1)
.flatMap(statelessMapper2);
messageStream.keyBy(Envelope::getId)
.process(new EnvelopeMapper(parameters))
.addSink(kafkaSink);
Is there any difference in performance if keyBy() is done right after reading from Kakfa (marked with "Line x") vs right before the stateful Mapper (EnvelopeMapper).
With
reinterpretAsKeyedStream(
DataStream<T> stream,
KeySelector<T, K> keySelector,
TypeInformation<K> typeInfo)
you are asserting that the records are already distributed exactly as they would be if you had instead used keyBy(keySelector). This will not normally be the case with records coming straight out of Kafka. Even if they are partitioned by key in Kafka, the Kafka partitions won't be correctly associated with Flink's key groups.
reinterpretAsKeyedStream is only straightforwardly useful in cases such as handling the output of a window or process function where you know that the output records are key partitioned in a particular way. To use it successfully with Kafka is can be very difficult: you must either be very careful in how the data is written to Kafka in the first place, or do something tricky with the keySelector so that the keyGroups it computes line up with how the keys are mapped to Kafka partitions.
One case where this isn't difficult is if the data is written to Kafka by a Flink job running with the same configuration as the downstream job that is reading the data and using reinterpretAsKeyedStream.
KeyedProcessFunction requires the previous operator to be a keyedBy operator
When I try to process a keyed stream using two KeyedProcessFunctions, why does the second function require me to apply the keyedBy operation again. Shouldn't the stream already be partitioned by keys?
var stream = env.addSource(new FlinkKafkaConsumer[Event]("flinkkafka", EventSerializer, properties))
var processed_stream_1 = stream
.keyBy("keyfield")
.process(new KeyedProcess1())
var processed_stream_2 = processed_stream_1
.process(new KeyedProcess2()) //this doesn't work
With some Flink operations, such as windows and process functions, there is a sort of disconnect between the input and output records, and Flink isn't able to guarantee that the records being emitted still follow the original key partitioning. If you are confident that it's safe to do so, you can use reinterpretAsKeyedStream instead of a second keyBy in order avoid an unnecessary network shuffle.
below is the pseudocode of my stream processing.
Datastream env = StreamExecutionEnvironment.getExecutionEnvironment()
env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime)
Datastream stream = env.addSource() .map(mapping to java object)
.filter(filter for specific type of events)
.assignTimestampsAndWatermarks(new BoundedOutOfOrdernessTimestampExtractor(Time.seconds(2)){})
.timeWindowAll(Time.seconds(10));
//collect all records.
Datastream windowedStream = stream.apply(new AllWindowFunction(...))
Datastream processedStream = windowedStream.keyBy(...).reduce(...)
String outputPath = ""
final StreamingFileSink sink = StreamingFileSink.forRowFormat(...).build();
processedStream.addSink(sink)
The above code flow is creating multiple files and each file has records of different windows I guess. For example, records in each files have timestamps which ranges between 30-40 seconds, whereas window time is only 10 seconds.
My expected output pattern is writing each window data to separate file.
Any references or input on this would be of great help.
Take a look at the BucketAssigner interface. It should be flexible enough to meet your needs. You just need to make sure that your stream events contain enough information to determine the path you want them written to.
For example, there is a keyed stream:
val keyedStream: KeyedStream[event, Key] = env
.addSource(...)
.keyBy(...)
// several transformations on the same stream
keyedStream.map(....)
keyedStream.window(....)
keyedStream.split(....)
keyedStream...(....)
I think this is the reuse of same stream in Flink, what I found is that when I reused it, the content of stream is not affected by the other transformation, so I think it is a copy of a same stream.
But I don't know if it is right or not.
If yes, this will use a lot of resources(which resources?) to keep the copies ?
A DataStream (or KeyedStream) on which multiple operators are applied replicates all outgoing messages. For instance, if you have a program such as:
val keyedStream: KeyedStream[event, Key] = env
.addSource(...)
.keyBy(...)
val stream1: DataStream = keyedStream.map(new MapFunc1)
val stream2: DataStream = keyedStream.map(new MapFunc2)
The program is executed as
/-hash-> Map(MapFunc1) -> ...
Source >-<
\-hash-> Map(MapFunc2) -> ...
The source replicates each record and sends it to both downstream operators (MapFunc1 and MapFunc2). The type of the operators (in our example Map) does not matter.
The cost of this is sending each record twice over the network. If all receiving operators have the same parallelism it could be optimized by sending each record once and duplicating it at the receiving task manager, but this is currently not done.
You manually optimize the program, by adding a single receiving operator (e.g., an identity Map operator) and another keyBy from which you fork to the multiple receivers. This will not result in a network shuffle, because all records are already local. All operator must have the same parallelism though.