I would like to make a Web Dashboard system and I am facing a problem. I need to get an information that is in the cache of one of the instances of my program, for this I had thought of doing Pub/Sub with Kafka however I don't know how to do to Publish and get a response from one of my Subscriber. Do you know a pattern that allows this and a service that allows me to do this?
EDIT: I would like to design an infrastructure that follows this pattern:
Attached diagram is showing simple request->response flow, Kafka is designed for different types of architecture, so IMHO you should not focus on Kafka in this case.
However, if you still want to use Kafka for some other reasons I can suggest to you two options:
Stick with request->response flow and use ReplyingKafkaTemplate or AggregatingKafkaTemplate to handle it, second one is an extension of first one, this adds functionality to handle more responses then one. You can send a request to Kafka topic from the Dashboard application, then poll the message by one of the Bot instances, next, send reply to reply topic, and then process reply in Dashboard application.
Use Kafka to implement Event-Carried State Transfer pattern, move state (mutual guilds data) from Bot Instances directly to Dashboard application via Kafka topic. You can use several tools to implement this:
Bot applications send events to Kafka topic via simple KafkaProducer or KafkaTemplate, then use one of the Kafka Connect sink connectors to save data in Dashboards database.
Bot applications send events to Kafka topic via simple KafkaProducer or KafkaTemplate. Run Kafka Streams thread in Dashboard application and build a state using Kafka Streams functionalities - grouping, aggregating etc. Then read the state directly from Kafka Streams internal RocksDB database.
I am using Kafka distributed system for message processing in spring boot application. Now my application are producing messages on even basic to three different different topics. There is one separate spring boot application which will be used by some data analysis team who will analysis the data. This application is a simple report type application with only one filter Topic.
Now I have to implement this but I am little bit confused how I will show the data to the UI. I have written listeners (Consumers) who are consuming the messages but how I will show the data to the UI on real time basic. Should I need to store it in some database like redis and then show this data to UI? Is this the correct way to deal with consumer in Kafka? Will it not be slow? As messages can grow drastically over the time.
In nutshell I want to know to how we can show messages on any UI in the efficient way and in real time.
Thanks
You can write a consumer to forward to a websocket.
Or you can use Kafka Connect to write to a database, then write a REST API
Or use Kafka Streams Interactive Queries feature + add a RPC layer on top for Javascript to call
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I want to learn Apache Kafka. I read articles and documents but I could not figure out how Kafka works. There are lots of questions in my mind :( I want to create a Kafka cluster and develop some code for preparing data engineering interviews. But, I am stuck. Any help would be appreciated. I will try to explain my questions in an example scenario.
For instance, there is a popular e-commerce company. They have a huge amount of web traffic. The web site is running on AWS. The mobile applications are also using AWS services.
The marketing department wants to observe the efficiency of their advertisement actions like email, SMS etc. They also want to follow important real-time metrics (sold products, page views, active users in the last n minutes etc) in a dashboard.
First, the campaign automation system sends personalized campaign emails to target customers. When a user clicks the link in the advertisement email, the browser is opening the e-commerce web site.
On the background, the website developers should send a clickstream event to the Kafka cluster with the related parameters (like customer id, advertisement id, source_medium etc).
How can the backend developers send a message to the Kafka cluster when a user loads the web site? Should developers send a post request or get request? Are they other alternatives?
Then data engineers should direct this clickstream message to the storage layer. (for example AWS S3). Will this cause too many small files in AWS S3 buckets? May this slow down the execution of data flows?
Data engineers need to develop a data pipeline in order to do real-time analysis. Which technologies should data engineers use? (Kafka Connect, Kafka Streams, Producer and Consumer etc)
Kafka topics may have lots of messages. One message can be consumed by different consumers. A consumer reads the message from the Kafka topic. Then, another consumer can read it, even after a while. So data engineers need to manage offsets in order to consume all messages one and only one. How can they manage offsets properly?
All clickstream events should be consumed.
All clickstream events should be consumed for once. If a product view event is consumed more than once, the dashboard will not show the correct product view count.
Do developers need to manage offsets manually? Or is there any technology/way which manages offsets automatically?
Event order can be important. The marketing department wants to see the category view durations. For instance, a user views 10 books in the ebooks category. Ten events were created. User is on the same category page until his/her first action. So data engineers need to calculate the duration between the first event and the last event.
However, Kafka is a queue and there is not any order in it. Producers can send data to Kafka asynchronously. How can data engineers calculate the durations correctly?
What happens if a producer sends an event to Kafka after the total elapsed duration was calculated.
Note: View duration may fit better to content web sites. For example, Netflix marketing users want to analyze the content view durations and percentages. If a user opens a movie and watched just five minutes, the marketing department may consider that the user does not like the movie.
Thanks in advance
You have really asked several unrelated questions here. Firstly, Kafka has a lot of free documentation available for it, along with many high quality 'getting started' blocks and paid books and courses. I would definitely start there. You might still have questions, but at least you will have a better awareness of the platform and you can ask questions in a lot more focused ways, which will hopefully get a much better answer. Start with the official docs. Personally, I learned Kafka by reading the Effective Kafka book, but I'm sure there are many others.
Going through your list of questions.
How can the backend developers send a message to the Kafka cluster when a user loads the web site? Should developers send a post request or get request? Are they other alternatives?
The website would typically publish an event. This is done by opening a client connection to a set of Kafka brokers and publishing a record to some topic. You mention POST/GET requests: this is not how Kafka generally works — the clients establish persistent connections to a cluster of brokers. However, if you preferred programming model is REST, Confluent does provide a Kafka REST Proxy for this use case.
Then data engineers should direct this clickstream message to the storage layer. (for example AWS S3). Will this cause too many small files in AWS S3 buckets? May this slow down the execution of data flows?
It depends how you write to S3. You may develop a custom consumer application that stages writes in a different persistent layer and then writes to S3 in batches. Kafka Connect also has an Amazon S3 connector that moves data in chunks.
Data engineers need to develop a data pipeline in order to do real-time analysis. Which technologies should data engineers use? (Kafka Connect, Kafka Streams, Producer and Consumer etc)
There is no correct answer here. All of the technologies you have listed are valid and may be used to a similar effect. Both Connect and Streams are quite popular for this types of applications; however, you can just as easily write a custom consumer application for all your needs.
Kafka topics may have lots of messages. One message can be consumed by different consumers. A consumer reads the message from the Kafka topic. Then, another consumer can read it, even after a while. So data engineers need to manage offsets in order to consume all messages one and only one. How can they manage offsets properly?
In the simplest case, Kafka offset management is automatic and the default behaviour allows for at-least once delivery, whereby a record will be delivered again if the first processing attempt failed. This may lead to duplicate effects (counting a clickstream event twice, as you described) but this is addressed by making your consumer idempotent. This is a fairly complex topic; there is great answer on Quora that covers the issue of exactly-once delivery in detail.
Event order can be important. The marketing department wants to see the category view durations. For instance, a user views 10 books in the ebooks category. Ten events were created. User is on the same category page until his/her first action. So data engineers need to calculate the duration between the first event and the last event.
The concept of order is backed into Kafka. Kafka's topics are sharded into partitions, where each partition is a totally-ordered, unbounded stream of records. Records may be strictly ordered provided they are published to the same partition. This is achieved by assigning them the same key, which the Kafka client hashes behind the scenes to arrive at the partition index. Any two records that have the same key will occupy the same partition, and will therefore be ordered.
Welcome to stackoverflow! I will answer a few of your questions, however you should go through the Kafka documentation for such things, if you are facing any problem while implementing it, then you should post here.
How can developers send data to a Kafka cluster? You have talked about producers, but I guess you haven't read about them, the developers will have to use a producer to produce an event to a Kafka topic.You can read more about a Kafka producer in the documentation.
To direct the messages to a storage layer, Kafka consumers will be used.
Note : Kafka Connect can be used instead of Kafka producer and consumer in some scenarios, Kafka connect has source connectors and sink connectors instead of producer and consumer.
For real time data analysis, Kafka Streams or KSQL can be used. These cannot be explained in an answer, I recommend you go through the documentation.
A single Kafka topic can have multiple consumer groups, and every consumer group has a different offset, you can tweak the configuration to use or not to use these offsets for every consumer group.
You can change various configurations such as Ack = All, to guarantee at least once and at most once semantics. Again you should go through the documentation to understand this completely.
You can maintain message order in Kafka as well, for that to happen, your consumers will have to wait for the acknowledgement from Kafka after every message has been sent, obviously this will slow down the process but you will have to compromise one of the things.
I haven't understood your requirements related to the last point, but I guess you should go through Kafka Streams and KSQL documentation once, as you can manage your window size for analysis over there.
I have tried to answer most of your questions in brief but to understand it completely, obviously you will have to go through the documentation in detail.
Agree with the answers above. The questions you ask are reasonably straightforward and likely answered in the official documentation.
As per one of the replies, there are lots of excellent books and tutorials online. I recently wrote a summary of educational resources on Kafka which you might find useful.
Based on your scenario, this will be a straightforward stream processing application with an emitter and a few consumers.
The clickstream event would be published onto the Kafka cluster through a Kafka client library. It's not clear what language the website is written in, but there is likely a library available for that language. The web server connects to Kafka brokers and publishes a message every time the user performs some action of significance.
You mention that order matters. Kafka has inherent support for ordered messages. All you need to do is publish related messages with the same key, for example the username of the customer or their ID. Kafka then ensures that those messages will appear in the order that they were published.
You say that multiple consumers will be reading the same stream. This is easily achieved by giving each set of consumers a different group.id. Kafka keeps a separate set of committed offsets for each consumer group (Kafka's terminology for a related set of consumers), so that one group can process messages independently of another. For committing offsets, the easiest approach is to use the automatic offset commit mode that is enabled by default. This way records will not be committed until your consumer is finished with them, and if a consumer fails midway through processing a batch of records, those records will be redelivered.
I am new to Kafka and data streaming and need some advice for the following requirement,
Our system is expecting close to 1 million incoming messages per day. The message carries a project identifier. The message should be pushed to users of only that project. For our case, lets say we have projects A, B and C. Users who opens project A's dashboard only sees / receives messages of project A.
This is my idea so far on implementing solution for the requirement,
The messages should be pushed to a Kafka Topic as they arrive, lets call this topic as Root Topic. The messages once pushed to the Root Topic, can be read by a Kafka Consumer/Listener and based on the project identifier in the message can push that message to a project specific Topic. So any message can end up at Topic A or B or C. Thinking of using websockets to update the message as they arrive on the project users' dashboards. There will be N Consumers/Listeners for the N project Topics. These consumers will push the project specific message to the project specifc websocket endpoints.
Please advise if I can make any improvements to the above design.
Chose Kafka as the messaging system here as it is highly scalable and fault tolerant.
There is no complex transformation or data enrichment before it gets sent to the client. Will it makes sense to use Apache Flink or Hazelcast Jet for the streaming or Kafka streaming is good enough for this simple requirement.
Also, when should I consider using Hazelcast Jet or Apache Flink in my project.
Should i use Flink say when I have to update few properties in the message based on a web service call or database lookup before sending it to the users?
Should I use Hazelcast Jet only when I need the entire dataset in memory to arrive at a property value? or will using Jet bring some benefits even for my simple use case specified above. Please advise.
Kafka Streams are a great tool to convert one Kafka topic to another Kafka topic.
What you need is a tool to move data from a Kafka topic to another system via web sockets.
Stream processor gives you a convenient tooling to build this data pipeline (among others connectors to Kafka and web sockets and scalable, fault-tolerant execution environment). So you might want use stream processor even if you don't transform the data.
The benefit of Hazelcast Jet is it's embedded scalable caching layer. You might want to cache your database/web service calls so that the enrichment is performed locally, reducing remote service calls.
See how to use Jet to read from Kafka and how to write data to a TCP socket (not websocket).
I would like to give you another option. I'm not Spark/Jet expert at all, but I've studying them for a few weeks.
I would use Pentaho Data Integration(kettle) to consume from the Kafka and I would write a kettle step (or User Defined Java Class step) to write the messages to a Hazelcast IMAP.
Then, would use this approach http://www.c2b2.co.uk/middleware-blog/hazelcast-websockets.php to provided the Websockets for the end-users.
We're considering using Kafka as a way to store all our business events forever. The purpose is to be able to spin up new "microservices" that we haven't yet thought of that will be able to leverage on all previous events to build up their projections/state. Another use case might be an existing service where we'd like to "replay" all events that is of interest to this service to recreate its state.
Note that we're not planning to use Kafka as an "event store" in the sense that events will be projected/loaded into an aggregate on "every request".
Also (as far as I can tell) we don't know how consumers will consume the events. A new microservice might need all sorts of different events in order to create its internal projection/state.
Is Kafka suitable for this or is there a better alternative?
If so, what's a good way to model this (topics/partitions)?
We're currently using RabbitMQ for messaging (business events are sent to RabbitMQ). It would be great if we could migrate away from RabbitMQ in the future and move entirely to Kafka. I assume that this could change the way topics and partitions are modelled since now we have a better understanding of how consumers will consume the events. Would this be compatible with the other use case (infinite retention and replay)?
This is very good that you are switching to KAFKA and Yes it is possible to keep data in KAFKA BROKERs but i would suggest rather than keeping all the data in KAFKA-BROKERs for all time why can't you dump this data into HDFS or S3(AWS) it will be cheaper and you will have all the features of HDFS available with your data.
Storing all data in Brokers will increase overhead on Zookeeper as well.