I have a simple kafka stream app that process input from 3 topics but no out is required since the final step is to save the processing outcome to db.
I saw this example for multiple inputs topics and that exactly what I need apart from the output binding, That is the bean should not return an output but should consume from 3 different topics as KStream,KTable and GlobalKtable and finish.
Any ideas how I can change the example for my needs?
Related
I have a kafka stream implemented using low level processor api with an intermediate topic. I expect the kafka stream to be split in two sub topologies , one from source to intermediate topic and other from intermediate topic to sink.
When I do topology.describe() I only see 1 sub topology created.
I know this can be done in DSL via through method but coudn't find an example with low level processor api.
Right now I am doing like this:
Topology topology = streamsBuilder.build();
topology.addSource("src",Serializer<MySer>,Deserializer<MyDeser>,"topic")
.addProcessor("processor1",MyProcessor::new,"src")
// intermediate topic starts
.addSink("intermediateTopicSink","intermediateTopicName",Serializer<MySer>,Deserializer<MyDeser>,"processor1")
.addSource("intermediateTopicSource",Serializer<MySer>,Deserializer<MyDeser>,"intermediateTopicName")
// intermediate topic ends
.addProcessor("processor2",AnotherProcessor::new,"intermediateTopicSource")
.addSink("finalSink","finalSinkTopic",Serializer<MySer>,Deserializer<MyDeser>,"processor2");
I am new to KStream and would like to know best practices or guidance on how to optimally process batch of records of n size using KStream. I have a working code as shown below but it does work for single messages at a time.
KStream<String, String> sourceStream = builder.stream("upstream-kafka-topic",
Consumed.with(Serdes.String(),
Serders.String());
//transform sourceStream using implementation of ValueTransformer<String,String>
sourceStream.transformValues(() -> new MyValueTransformer()).
to("downstream-kafka-topic",
Produced.with(Serdes.String(),
Serdes.String());
Above code works with single records as MyValueTransformer which implements ValueTransformer transforms single String value. How do I make above code work for Collection of String values?
You would need to somehow "buffer / aggregate" the messages. For example, you could add a state store to your transformer and store N messages inside the store. As long as the store contains fewer than N messages you don't do any processing and also don't emit any output (you might want to use flatTransformValues which allows you to emit zero results).
Not sure what you're trying to achieve. Kafka Streams by concept is designed to process one record at a time. If you want to process a collection or batch of messages you have a few options.
You might not actually need Kafka streams as the example you mentioned doesn't do much with the message, in this case, you can leverage a normal Consumer which will enable you to process in Batches. Check spring Kafka implementation of this here -> https://docs.spring.io/spring-kafka/docs/current/reference/html/#receiving-messages (Kafka process batches on the network layer but normally you would process one record at a time, but it's possible with a standard client to process batches) OR you might model your Object value to have an array of messages so for each record you will be receiving an object which contains a collection embedded which you could then use Kafka streams to do it, check the array type for Avro -> https://avro.apache.org/docs/current/spec.html#Arrays
Check this part of the documentation to understand better the Kafka streams concepts -> https://kafka.apache.org/31/documentation/streams/core-concepts
It is my first post to this here and I am not sure if this was covered here before, but here goes: I have a Kafka Streams application, using Processor API, following the topology below:
1. Consume data from an input topic (processor.addSource())
2. Inserts data into a DB (processor.addProcessor())
3. Produce its process status to an output topic (processor.addSink())
App works big time, however, for traceability purposes, I need to have in the logs the moment kstreams produced a message to the output topic, as well as its RecordMetaData (topic, partition, offset).
Example below:
KEY="MY_KEY" OUTPUT_TOPIC="MY-OUTPUT-TOPIC" PARTITION="1" OFFSET="1000" STATUS="SUCCESS"
I am not sure if there is a way to override the default kafka streams producer to add this logging or maybe creating my own producer to plug it on the addSink process. I partially achieved it by implementing my own ExceptionHandler (default.producer.exception.handler), but it only covers the exceptions.
Thanks in advance,
Guilherme
If you configure the streams application to use a ProducerInterceptor, then you should be able to get the information you need. Specifically, implementing the onAcknowledgement() will provide access to everything you listed above.
To configure interceptors in a streams application:
Properties props = new Properties();
// add this configuration in addition to your other streams configs
props.put(StreamsConfig.producerPrefix(ProducerConfig.INTERCEPTOR_CLASSES_CONFIG), Collections.singletonList(MyProducerInterceptor.class));
You can provide more than one interceptor if desired, just add the class name and change the list implementation from a singleton to a regular List. Execution of the interceptors follows the order of the classes in the list.
EDIT: Just to be clear, you can override the provided Producer in Kafka Streams via the KafkaClientSupplier interface, but IMHO using an interceptor is the cleaner approach. But which direction to go is up to you. You pass in your KafkaClientSupplier in an overloaded Kafka Streams constructor.
I have a Storm topology where I have to send output to kafka as well as update a value in redis. For this I have a Kafkabolt as well as a RedisBolt.
Below is what my topology looks like -
tp.setSpout("kafkaSpout", kafkaSpout, 3);
tp.setBolt("EvaluatorBolt", evaluatorBolt, 6).shuffleGrouping("kafkaStream");
tp.setBolt("ResultToRedisBolt",ResultsToRedisBolt,3).shuffleGrouping("EvaluatorBolt","ResultStream");
tp.setBolt("ResultToKafkaBolt", ResultsToKafkaBolt, 3).shuffleGrouping("EvaluatorBolt","ResultStream");
The problem is that both of the end bolts (Redis and Kafka) are listening to the same stream from the preceding bolt (ResultStream), hence both can fail independently. What I really need is that if the result is successfully published in Kafka, then only I update the value in Redis. Is there a way to have an output stream from a kafkaBolt where I can get the messages published successfully to Kafka? I can then probably listen to that stream in my RedisBolt and act accordingly.
It is not currently possible, unless you modify the bolt code. You would likely be better off changing your design slightly, since doing extra processing after the tuple is written to Kafka has some drawbacks. If you write the tuple to Kafka and you fail to write to Redis, you will get duplicates in Kafka, since the processing will start over at the spout.
It might be better, depending on your use case, to write the result to Kafka, and then have another topology read the result from Kafka and write to Redis.
If you still need to be able to emit new tuples from the bolt, it should be pretty easy to implement. The bolt recently got the ability to add a custom Producer callback, so we could extend that mechanism.
See the discussion at https://github.com/apache/storm/pull/2790#issuecomment-411709331 for context.
I'm trying to setup Storm to aggregate a stream, but with various (DRPC available) metrics on the same stream.
E.g. the stream is consisted of messages that have a sender, a recipient, the channel through which the message arrived and a gateway through which it was delivered. I'm having trouble deciding how to organize one or more topologies that could give me e.g. total count of messages by gateway and/or by channel. And besides the total, counts per minute would be nice too.
The basic idea is to have a spout that will accept messaging events, and from there aggregate the data as needed. Currently I'm playing around with Trident and DRPC and I've came up with two possible topologies that solve the problem at this stage. Can't decide which approach is better, if any?!
The entire source is available at this gist.
It has three classes:
RandomMessageSpout
used to emit the messaging data
simulates the real data source
SeparateTopology
creates a separate DRPC stream for each metric needed
also a separate query state is created for each metric
they all use the same spout instance
CombinedTopology
creates a single DRPC stream with all the metrics needed
creates a separate query state for each metric
each query state extracts the desired metric and groups results for it
Now, for the problems and questions:
SeparateTopology
is it necessary to use the same spout instance or can I just say new RandomMessageSpout() each time?
I like the idea that I don't need to persist grouped data by all the metrics, but just the groupings we need to extract later
is the spout emitted data actually processed by all the state/query combinations, e.g. not the first one that comes?
would this also later enable dynamic addition of new state/query combinations at runtime?
CombinedTopology
I don't really like the idea that I need to persist data grouped by all the metrics since I don't need all the combinations
it came as a surprise that the all the metrics always return the same data
e.g. channel and gateway inquiries return status metrics data
I found that this was always the data grouped by the first field in state definition
this topic explains the reasoning behind this behaviour
but I'm wondering if this is a good way of doing thins in the first place (and will find a way around this issue if need be)
SnapshotGet vs TupleCollectionGet in stateQuery
with SnapshotGet things tended to work, but not always, only TupleCollectionGet solved the issue
any pointers as to what is correct way of doing that?
I guess this is a longish question / topic, but any help is really appreciated!
Also, if I missed the architecture entirely, suggestions on how to accomplish this would be most welcome.
Thanks in advance :-)
You can't actually split a stream in SeparateTopology by invoking newStream() using the same spout instance, since that would create new instances of the same RandomMessageSpout spout, which would result in duplicate values being emitted to your topology by multiple, separate spout instances. (Spout parallelization is only possible in Storm with partitioned spouts, where each spout instance processes a partition of the whole dataset -- a Kafka partition, for example).
The correct approach here is to modify the CombinedTopology to split the stream into multiple streams as needed for each metric you need (see below), and then do a groupBy() by that metric's field and persistentAggregate() on each newly branched stream.
From the Trident FAQ,
"each" returns a Stream object, which you can store in a variable. You can then run multiple eaches on the same Stream to split it, e.g.:
Stream s = topology.each(...).groupBy(...).aggregate(...)
Stream branch1 = s.each(...)
Stream branch2 = s.each(...)
See this thread on Storm's mailing list, and this one for more information.