I am working on a project which is actually will be a better version of an old project. We want it to be scalable to be able to deal with high load. So we decided to go with microservices instead of monolithic. Then I started to do research about microservices, how they communicate, common design patterns and other things. Since I want my services to be scalable, event based communication made sense to me. So I decided to use kafka for this purpose.
We have much more services in the system but to simplify my question lets say I have 2 types of services which are work-node and master-node. I want both of them to be scalable. For now they are communicating over kafka.
My question : for a case I want to publish an event (produce a message on a topic) from master-node and get that event (consume from the topic) from all work-nodes. But for an other case I need to send a message to specific work-node. To be able to cover first case, all my work-nodes have different group ids in kafka and when a message published on a topic they all get that message. I know that I am not able to send a message to specific consumer with kafka. Since my nodes are scalable and their number can increase or decrease depending on the load, creating a topic for each node does not seem a good idea. My first solution was adding work-node id in message. So other work-nodes can ignore that message. Well it works but I don't think it is a good solution. My second solution is sending http request if I am going to send a message to specific node. But I don't know mixing 2 communication methods is a good solution.
What do you guys think about this problem. Is there a better solution that I am missing ? Or my whole design is going wrong ?
Kafka is not an appropriate technology for the use case you describe. I would recommend using Cadence Workflow which natively supports routing tasks to specific nodes as well as dozens of other features that messaging systems lack.
Feel free to join Cadence Workflow slack channel if you have specific questions.
I think you should able to. Consider regular Kafka flow. You have some consumer groups subscribed to the topic. Producer doesn't send message to specific partition until you specify.
Now think about the scenario that you produce some message based on your algorithm to the specific partitions.
Message received from A
some kind of algorithm like hashcode generated always 0 for A
Message send to Partition 0
Consumer 1 connected to Partiton 0
Only Consumer 1 gets the message coming from A
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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.
So I am currently working on a chat, and I wonder if I could use Redis to store the chat messages. The messages will be only at the web and I want at least a chat history of 20 messages for each private chat. The Chats subscribers will be already stored in MongoDB.
I mainly want to use Redis, because I get rid of the MongoDB stuff, for more speed.
I already use Pub/Sub, but what about storing a copy in Redis Lists? Also what about reading statuses, how could I implement that?
Redis only loses data in case of power outage, if the system is shutdown properly, it will save its data and in this case, data won't be lost.
It is good approach to dump data from redis to mongoDb/anyotherDb when a size limit is reached or on date basis (weekly or monthly) so that your realtime chat database stays light weighted.
Many modern systems now a days prepare for power outage, a ups will run and the system will shutdown properly.
see : https://hackernoon.com/how-to-shutdown-your-servers-in-case-of-power-failure-ups-nut-co-34d22a08e92
Also what about reading statuses, how could I implement that?
Depends on protocol you are implementing, if you are using xmpp, see this.
Otherwise, you can use a property in message model for e.g "DeliveryStatus" and set it to your enums (1. Sent, 2. Delivered, 3. Read). Mark message as Sent as soon as it is received at server. For Delivered and Read, your clients will send you back packets indicating the respective action has occurred.
As pointed in the comment above, the important thing to consider here is the persistency model. Redis offers some persistency (with snapshots and aof-files). The important thing is to first understand what you need:
can you afford to lose all the data? can you afford to lose some of the data? if the answer is no, then perhaps you should not bother with redis.
I'm not sure if this is possible or not. If I set a number of messages to be persisted under a topic for some period of time, can I later grab all of them?
I have an MQTT Broker (Mosquitto) set up already for communication between my services but I now also need some storage for several messages, ideally keeping the last 24 hours worth of messages and being able to pull them out later.
Message persistence is only for clients that have subscribed but are currently disconnected and when they do reconnect do so with the cleanSession flag set to false. In which case all the messages published while that client was disconnected.
You can not use a MQTT broker to store an arbitrary number of messages and retrieve them later. If a client is connected then all messages for it's collection of subscribed topics will be delivered as soon as possible.
Of you want to log messages for later you will have to implement this separately, there are plenty of examples of applications that store messages in databases available
I'm using node-amqp. For each queue, there is one sender and one consumer. On the sender side, I need to maintain a list of active consumers. The question is when a consumer computer crashed, how would I get a notification and delete it from the list at the sender side?
I think you may not be using the MQ concept correctly. The whole point is to disconnect the consumers from the producers. On the whole it is not the job of the producers to know anything about the consumers, except the type of message they will be consuming. To the point that the producer will keep producing if a consumer crashes and the messages will continue to build up in the queue it was reading from.
There is a way to do it by using RabbitMQ's HTTP API (at http://server-name:55672/api/) to get list of connections, but it is too brutal for frequently queries. Another way in theory is to use alternate exchanges to detect undelivered messages, but I didn't tried this way yet.
Also, it may be possible to detect unexpected consumer disconnection by using dead-letter-exchanges as described there: http://www.rabbitmq.com/dlx.html