I know about what is Producer and Consumer. But official documentation says
It is streaming platform.
It is enterprise messaging system.
Kafka has connectors which are import and export data from databases and other system also.
What does it mean?
I know Producers are client applications which send data to Kafka Broker and Consumers are also client applications which read data from Kafka Broker.
But my question is, can a Consumer push data into Kafka Broker?
And as per my knowledge, I assume that if Consumer wants to push data into Kafka Broker, it becomes a Producer. Is that correct?
1.It is a streaming platform.
It is used for distribution of a data on a public-subscriber model with a storage layer and processing layer.
2.It is an enterprise messaging system.
Big Data infrastructure is open source, so big data market cost per year approximately $40B and may be increased day by day. So it has come to host of hardware. Despite the open source nature of much of his software, there's a lot of money to be made.
3.Kafka has connectors which are import and export data from databases
and other systems also.
Kafka connect provides connectors i.e. Source connector, Sink Connector, JDBC Connector. It provides a facility to importing data from sources and exporting it to multiple targets.
Producers: It can only push data to a Kafka broker or we can say publish data.
Consumers: It can only pull data from the Kafka broker.
A producer produces/puts/publishes messages and as consumer consumes/gets/reads messages.
A consumer can only read, when you want to write you need a producer. A consumer cannot become a producer.
A producer only push data to a Kafka broker.
A consumer only pull data from a Kafka broker.
However, you can have a program being both, a producer and a consumer.
Related
I have to publish messages from a kafka topic to lambda to process them and store in a database using a springboot application, i did some research and found something to consume messages from kafka
public Function<KStream<String, String>, KStream<String, String>> process(){} however, im not sure if this is only used to publish the consumed messages to another kafka topic or can be used as an event source to lambda, I need some guidance on consuming and converting the consumed kafka message to event source.
Brokers do not push. Consumers always poll.
Code shown is for Kafka Streams API, which primarily writes to new Kafka topics. While you could fire HTTP events to start a lambda, that's not recommended.
Alternatively, Kafka is already supported as an event source. You don't need to write any consumer code.
https://aws.amazon.com/about-aws/whats-new/2020/12/aws-lambda-now-supports-self-managed-apache-kafka-as-an-event-source/
This is possible from MSK or a self managed Kafka
process them and store in a database
Your lambda could process the data and send to a new Kafka topic using a producer. You can then use MSK Connect or run your own Kafka Connect cluster elsewhere to dump records into a database. No Spring/Java code would be necessary.
I need to push data from database(say Oracle DB) to a kafka topic by calling a stored procedure.I need to do some validations too on message
Should i use a Producer API or Kafka Connector.
You should use Kafka Connect. Either that, or use the producer API and write some code that handles:
Scale-out
Failover
Restarts
Schemas
Serialisation
Transformations
and that integrates with hundreds of other technologies in a standardised manner for ease of portability and management.
At which point, you'll have reinvented Kafka Connect ;-)
To learn more about Kafka Connect see this talk. To learn about the specifics of database->Kafka ingestion see this talk and this blog.
I need to understand when to use Kafka connect vs. own consumer/producer written by developer. We are getting Confluent Platform. Also to achieve fault tolerant design do we have to run the consumer/producer code ( jar file) from all the brokers ?
Kafka connect is typically used to connect external sources to Kafka i.e. to produce/consume to/from external sources from/to Kafka.
Anything that you can do with connector can be done through
Producer+Consumer
Readily available Connectors only ease connecting external sources to Kafka without requiring the developer to write the low-level code.
Some points to remember..
If the source and sink are both the same Kafka cluster, Connector doesn't make sense
If you are doing changed-data-capture (CDC) from a database and push them to Kafka, you can use a Database source connector.
Resource constraints: Kafka connect is a separate process. So double check what you can trade-off between resources and ease of development.
If you are writing your own connector, it is well and good, unless someone has not already written it. If you are using third-party connectors, you need to check how well they are maintained and/or if support is available.
do we have to run the consumer/producer code ( jar file) from all the brokers ?
Don't run client code on the brokers. Let all memory and disk access be reserved for the broker process.
when to use Kafka connect vs. own consumer/produce
In my experience, these factors should be taken into consideration
You're planning on deploying and monitoring Kafka Connect anyway, and have the available resources to do so. Again, these don't run on the broker machines
You don't plan on changing the Connector code very often, because you must restart the whole connector JVM, which would be running other connectors that don't need restarted
You aren't able to integrate your own producer/consumer code into your existing applications or simply would rather have a simpler produce/consume loop
Having structured data not tied to the a particular binary format is preferred
Writing your own or using a community connector is well tested and configurable for your use cases
Connect has limited options for fault tolerance compared to the raw producer/consumer APIs, with the drawbacks of more code, depending on other libraries being used
Note: Confluent Platform is still the same Apache Kafka
Kafka Connect:
Kafka Connect is an open-source platform which basically contains two types: Sink and Source. The Kafka Connect is used to fetch/put data from/to a database to/from Kafka. The Kafka connect helps to use various other systems with Kafka. It also helps in tracking the changes (as mentioned in one of the answers Changed Data Capture (CDC) ) from DB's to Kafka. The system maintains the offset, in order to read/write data from that particular offset to Kafka or any other database.
For more details, you can refer to https://docs.confluent.io/current/connect/index.html
The Producer/Consumer:
The Producer and Consumer are just an end system, which use the Kafka to produce and consume topics to/from Kafka. They are used where we want to broadcast the data to various consumers in a consumer group. This kind of system also maintains the lag and offsets of data for the consumer groups.
No, you don't need to run any producer/consumer while running Kafka connect. In case you want to check there is no data loss you can run the consumer while running Source Connectors. In case, of Sink Connectors, the already produced data can be verified in your database, by running their particular select queries.
I am using Kafka 2 and looks like exactly once is possible with
Kafka Streams
Kafka read/transform/write transactional producer
Kafka connect
Here, all of the above works between topics (source and destination is topic).
Is it possible to have exactly once with other destinations?
Source and destinations (sinks) of Connect are not only topics, but which Connector you use determines the delivery semantics, not all are exactly once
For example, a JDBC Source Connector polling a database might miss some records
Sink Connectors coming out of Kafka will send every message from a topic, but it's up to the downstream system to acknowledge that retrieval
I have developed kafka-sink-connector (using confluent-oss-3.2.0-2.11, connect framework) for my data-store (Amppol ADS), which stores data from kafka topics to corresponding tables in my store.
Every thing is working as expected as long as kafka servers and ADS servers are up and running.
Need a help/suggestions about a specific use-case where events are getting ingested in kafka topics and underneath sink component (ADS) is down.
Expectation here is Whenever a sink servers comes up, records that were ingested earlier in kafka topics should be inserted into the tables;
Kindly advise how to handle such a case.
Is there any support available in connect framework for this..? or atleast some references will be a great help.
SinkConnector offsets are maintained in the _consumer_offsets topic on Kafka against your connector name and when SinkConnector restarts it will pick messages from Kafka server from the previous offset it had stored on the _consumer_offsets topic.
So you don't have to worry anything about managing offsets. Its all done by the workers in the Connect framework. In your scenario you go and just restart your sink connector. If the messages are pushed to Kafka by your source connector and are available in the Kafka, sink connector can be started/restarted at any time.