Is there any way to obtain lineage data for Kafka jobs.
Like for example we have a Job History URL for all the MapReduce jobs.
Is there anything similar for Kafka where I can get the metadata of the Producer producing information to a particular topic? (eg: IP address of the producer)
Out of the box, no, not for Producers.
You can list consumer client addressses, but not see producers into a topic.
One option would be to look into Apache Atlas for lineage information, which is one component of Hortonwork's Stream Messaging Manager for analyzing Kafka connection information.
Otherwise, you would be stuck trying to enfore producers to send this data along with the messages.
Related
I have started to learn about MQTT as I have a use case in telematics in my current organisation. I would like to integrate MQTT broker ( mosquitto ) messages to my kafka.
Since every vehicle is sending the data in its own topic in MQTT broker within a single organisation, I would like to push all this data in kafka. Now I know it is not advisable to create so many topics in kafka ( more than a million ). Also I would like not like to save all the vehicles data in one kafka topic as I would like to later put all this data in S3, differentiated via vehicle id.
How can I achieve this without making so many topics in kafka. One way is the consumer of kafka segregate the events and put in s3 but I believe there will be a lot of small files in S3.
Generally, if you have the same logical entity you would use the same topic.
You can use the MQTT plugin for Kafka Connect to stream the data from MQTT into Kafka, and Kafka Connect's Single Message Transform RegexRouter to modify the topic name to which messages are written, and other SMT to modify the message key. That way you get all the messages in one topic, partitioned based on the vehicle id. That's probably the best way to store it.
From there, you can use the data however you want. When it comes to stream it to S3, you can use Kafka Connect S3 sink and as cricket_007 mentioned partition the data by time if it's just volume you're worried about. If you want to route the messages to different buckets or areas of the same bucket you could use a stream processing (e.g. Kafka Streams / ksqlDB) to pre-process the topic to populate others.
See here for an example of the MQTT connector.
I have a requirement to send data from an external system to a Kafka topic with exactly once semantics.
The source has an offset, we can consume messages from a given offset.
Looking at Kafka documentation, I see there are 2 ways to do this.
Kafka Source Connector
Use plain Kafka producer with transactions.
It looks like option 1 doesn't support exactly once semantics now, Kafka jira 6080 is unresolved. Also I would like to understand how we can do this directly with the producer apis.
For option 2, the (consume, transform, produce) loop in all the documents show committing offsets of consumers using AddOffsetsToTxn. What is the recommended strategy if the source is not a Kafka topic? Looks like writing the source offset in a different topic as part of the transaction and using it during recovery would work. Is this the recommended way?
I am trying to setup a data pipeline using Kafka.
Data go in (with producers), get processed, enriched and cleaned and move out to different databases or storage (with consumers or Kafka connect).
But where do you run the actual pipeline processing code to enrich and clean the data? Should it be part of the producers or the consumers? I think I missed something.
In the use case of a data pipeline the Kafka clients could serve both as a consumer and producer.
For example, if you have raw data being streamed into ClientA where it is being cleaned before being passed to ClientB for enrichment then ClientA is serving as a consumer (listening to a topic for raw data) and a producer (publishing cleaned data to a topic).
Where you draw those boundaries is a separate question.
It can be part of either producer or consumer.
Or you could setup an environment dedicated to something like Kafka Streams processes or a KSQL cluster
It is possible either ways.Consider all possible options , choose an option which suits you best. Lets assume you have a source, raw data in csv or some DB(Oracle) and you want to do your ETL stuff and load it back to some different datastores
1) Use kafka connect to produce your data to kafka topics.
Have a consumer which would consume off of these topics(could Kstreams, Ksql or Akka, Spark).
Produce back to a kafka topic for further use or some datastore, any sink basically
This has the benefit of ingesting your data with little or no code using kafka connect as it is easy to set up kafka connect source producers.
2) Write custom producers, do your transformations in producers before
writing to kafka topic or directly to a sink unless you want to reuse this produced data
for some further processing.
Read from kafka topic and do some further processing and write it back to persistent store.
It all boils down to your design choice, the thoughput you need from the system, how complicated your data structure is.
I really want to get an architectural solution for my below scenario.
I have a source of events (Say sensors in oil wells , around 50000 ), that produces events to a server. At the server side I want to process all these events in such a way that , the information from the sensors about latest humidity, temperature,pressure ...etc will be stored/updated to a database.
I am confused with flume or kafka.
Can somebody please address my simple scenario in architectural terms.
I don't want to store the event somewhere, since I am already updating the database with latest values.
Should I really need spark , (flume/kafka) + spark , to meet the processing side?.
Can we do any kind of processing using flume without a sink?
Sounds like you need to use the Kafka producer API to publish the events to a topic then simply read those events either by using the Kafka consumer API to write to your database or use the Kafka JDBC sink connector.
Also if you need just the latest data inside Kafka take a look at log compaction.
One way would be to push all the messages to Kafka Topic. Using Spark Stream you can ingest and process from the kafka topic. Spark streaming can directly process from your Kafka Topic
I am building a simple application which does below in order -
1) Reads messages from a remote IBM MQ(legacy system only works with IBM MQ)
2) Writes these messages to Kafka Topic
3) Reads these messages from the same Kafka Topic and calls a REST API.
4) There could be other consumers reading from this topic in future.
I came to know that Kafka has the new streams API which is supposed to be better than Kafka consumer in terms of speed/simplicity etc. Can someone please let me know if the streams API is a good fit for my use case and at what point in my process i can plug it ?
It is true that Kafka Streams API has a simple way to consume records in comparison to Kafka Consumer API (e.g. you don't need to poll, manage a thread and loop), but it also comes with a cost (e.g. local data store - if you do stateful processing).
I would say that if you need to consume records one by one and call a REST API use the Consumer API, if you need stateful processing, query the topic state, etc. use the Streams API.
For more info take a look to this blog post: https://balamaci.ro/kafka-streams-for-stream-processing/
Reads messages from a remote IBM MQ (legacy system only works with
IBM MQ)
Writes these messages to Kafka Topic
I'd use Kafka Connect for (1) and (2). It is part of the Kafka project, and there are many free as well as commercial "connectors" available for hundreds of systems.
Reads these messages from the same Kafka Topic and calls a REST API.
You can use Kafka Streams as well as the lower-level Consumer API of Kafka, depending on what you prefer. I'd go with Kafka Streams as it is easier to use and far more powerful. (Both are part of the Kafka project.)
There could be other consumers reading from this topic in future.
This works out-of-the-box -- once data is stored in a Kafka topic according to step 2, many different applications and "consumers" can read this data independently.
Looks like you are not doing any processing/transformation once you consume you message from your IBM MQ or even after your Kafka Topic.
First one -> from IBM Mq to your Kafka Topic is kind of a pipeline and
Secondly -> You are just calling the REST API(I assume w/o any processing)
Considering these facts it seems to be a good fit for using Simple consumer.
Let's not use a technology only because it's there :)