Reading lagom documentation, we can build read side with both read side processor and message broker api.
What are advantages to use one over another?
(Let's take into account general ReadSide support (not Cassandra and Jdbc which implement event offset tracking).
Prototyping some application, I've noticed that Read Side Processor has higher latency between events persistence into event log and read model update than Broker Api.
Prototyping some application, I've noticed that Read Side Processor
has higher latency between events persistence into event log and read
model update than Broker Api.
This is the eventual consistency delay. You can change this with a setting in your application.conf:
cassandra-query-journal.eventual-consistency-delay = 1s
You have to decide what is necessary for your application. You can set this to zero, but it is rarely necessary.
Related
We had a use case where our client by mistake sends 10000 request, for each request we had to immediately send a ID and then enrich the data with multiple DB calls/rest call and finally response back to Kafka topic. Due to this much processing the whole system went down including the underlined system as along with Kakfa we also publish to MQ which again goes for further processing.
Ask is to control the number of requests client can send, we though of controlling it to store the threshold in DB based on per day or per hours and start rejecting once they reach the threshold but this requires computation and DB hits.
Is there any tool or out of box solution with minimum effort and without adding performance load to system? We are looking for some kind of Back pressure technique like in Spring webflux etc.
It is spring boot application on Java 11.
Backpressure works the other way around, when you are consuming a client that is emitting more than you can process. What you are looking for is Rate Limiter.
You can take a look at the Resilience4J Rate Limiter
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
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.
I need to fetch messages from Kafka topics and notify other systems via HTTP based APIs. That is, get message from topic, map to the 3rd party APIs and invoke them. I intend to write a Kafka Sink Connector for this.
For this use case, is Kafka Connect the right choice or I should go with Kafka Client.
Kafka clients when you have full control on your code and you are expert developer, you want to connect an application to Kafka and can modify the code of the application.
push data into Kafka
pull data from Kafka.
https://cwiki.apache.org/confluence/display/KAFKA/Clients
Kafka Connect when you don’t have control on third party code new in Kafka and to you have to connect Kafka to datastores that you can’t modify code.
Kafka Connect’s scope is narrow: it focuses only on copying streaming data to and from Kafka and does not handle other tasks.
http://docs.confluent.io/2.0.0/connect/
I am adding few lines form other blogs to explain differences
Companies that want to adopt Kafka write a bunch of code to publish their data streams. What we’ve learned from experience is that doing this correctly is more involved than it seems. In particular, there are a set of problems that every connector has to solve:
• Schema management: The ability of the data pipeline to carry schema information where it is available. In the absence of this capability, you end up having to recreate it downstream. Furthermore, if there are multiple consumers for the same data, then each consumer has to recreate it. We will cover the various nuances of schema management for data pipelines in a future blog post.
• Fault tolerance: Run several instances of a process and be resilient to failures
• Parallelism: Horizontally scale to handle large scale datasets
• Latency: Ingest, transport and process data in real-time, thereby moving away from once-a-day data dumps.
• Delivery semantics: Provide strong guarantees when machines fail or processes crash
• Operations and monitoring: Monitor the health and progress of every data integration process in a consistent manner
These are really hard problems in their own right, it just isn’t feasible to solve them separately in each connector. Instead you want a single infrastructure platform connectors can build on that solves these problems in a consistent way.
Until recently, adopting Kafka for data integration required significant developer expertise; developing a Kafka connector required building on the client APIs.
https://www.confluent.io/blog/announcing-kafka-connect-building-large-scale-low-latency-data-pipelines/
Kafka Connect will work well for this purpose, but this would also be a pretty straightforward consumer application as well because consumers also have the benefits of fault tolerance/scalability and in this case you're probably just doing simple message-at-a-time processing within each consumer instance. You can also easily use enable.auto.commit for this application, so you will not encounter the tricky parts of using the consumer directly. The main thing using Kafka Connect would give you compared to using the consumer in this case would be that the connector could be made generic for different input formats, but that may not be important to you for a custom connector.
you should use kafka connect sink when you are using kafka connect source for producing messages to a specific topic.
for e.g. when you are using file-source then you should use file-sink to consume what source have been produced. or when you are using jdbc-source you should use jdbc-sink to consume what you have produced.
because the schema of the producer and sink consumer should be compatible then you should use compatible source and sink in both sides.
if in some cases the schemas are not compatible you can use SMT (Simple message transform) capability that is added since version 10.2 of kafka onward and you will be able to write message transformers to transfer message between incompatible producers and consumers.
Note: if you want to transfer messages faster I suggest that you use avro and schema registry to transfer message more efficiently.
If you can code with java you can use java kafka stream, Spring-Kafka project or stream processing to achieve what you desire.
In the book that is called Kafka In Actionis explained like following:
The purpose of Kafka Connect is to help move data in or out of Kafka without having to deal with writing our own producers and clients. Connect is a framework that is already part of Kafka that really can make it simple to use pieces that have been already been built to start your streaming journey.
As for your problem, Firstly, one of the simpliest questions that one should ask is if you can modify the application code of the systems from which you need data interaction.
Secondly, If you would write custom connector which have the in-depth knowledge the ability and this connector will be used by others, it worth it. Because it may help others that may not be the experts in those systems. Otherwise, this kafka connector is used only by yourself, I think you should write Kafka connector. So you can get more flexibility and can write more easily implementing.
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I would like to ask if my understanding of Kafka is correct.
For really really big data stream, conventional database is not adequate so people use things such as Hadoop or Storm. Kafka sits on top of said databases and provide ...directions where the real time data should go?
I don't think so.
Kafka is messaging system and it does not sit on top of database.
You can compare Kafka with messaging systems like ActiveMQ, RabbitMQ etc.
From Apache documentation page
Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.
Key takeaways:
Kafka maintains feeds of messages in categories called topics.
We'll call processes that publish messages to a Kafka topic producers.
We'll call processes that subscribe to topics and process the feed of published messages consumers..
Kafka is run as a cluster comprised of one or more servers each of which is called a broker.
Communication between the clients and the servers is done with a simple, high-performance, language agnostic TCP protocol.
Use Cases:
Messaging: Kafka works well as a replacement for a more traditional message broker. In this domain Kafka is comparable to traditional messaging systems such as ActiveMQ or RabbitMQ
Website Activity Tracking: The original use case for Kafka was to be able to rebuild a user activity tracking pipeline as a set of real-time publish-subscribe feeds
Metrics: Kafka is often used for operational monitoring data, which involves aggregating statistics from distributed applications to produce centralized feeds of operational data
Log Aggregation
Stream Processing
Event sourcing is a style of application design where state changes are logged as a time-ordered sequence of records.
Commit Log: Kafka can serve as a kind of external commit-log for a distributed system. The log helps replicate data between nodes and acts as a re-syncing mechanism for failed nodes to restore their data
To fully understand Apache Kafka's role you should get a wider picture and know Kafka's use cases. Modern data processing systems try to break with the classic application architecture. You can start from the kappa architecture overview:
http://milinda.pathirage.org/kappa-architecture.com
In this architecture you don't store the current state of the world in any SQL or key-value database. All data is processed and stored as one or more series of events in an append-only immutable log. Immutable events are easier to replicate and store in a distributed environment. Apache Kafka is a system that is used storing these events and for brokering them between other system components.
Use cases on Apache Kafka's official site: http://kafka.apache.org/documentation.html#uses
More use cases :-
Kafka-Storm Pipeline -
Kafka can be used with Apache Storm to handle data pipeline for high speed filtering and pattern matching on the fly.
Apache Kafka is not just a message broker. It was initially designed and implemented by LinkedIn in order to serve as a message queue. Since 2011, Kafka has been open sourced and quickly evolved into a distributed streaming platform, which is used for the implementation of real-time data pipelines and streaming applications.
It is horizontally scalable, fault-tolerant, wicked fast, and runs in
production in thousands of companies.
Modern organisations have various data pipelines that facilitate the communication between systems or services. Things get a bit more complicated when a reasonable number of services needs to communicate with each other at real time.
The architecture becomes complex since various integrations are required in order to enable the inter-communication of these services. More precisely, for an architecture that encompasses m source and n target services, n x m distinct integrations need to be written. Also, every integration comes with a different specification, meaning that one might require a different protocol (HTTP, TCP, JDBC, etc.) or a different data representation (Binary, Apache Avro, JSON, etc.), making things even more challenging. Furthermore, source services might address increased load from connections that could potentially impact latency.
Apache Kafka leads to more simple and manageable architectures, by decoupling data pipelines. Kafka acts as a high-throughput distributed system where source services push streams of data, making them available for target services to pull them at real-time.
Also, a lot of open-source and enterprise-level User Interfaces for managing Kafka Clusters are available now. For more details refer to my answer to this question.
You can find more details about Apache Kafka and how it works in the blog post "Why Apache Kafka?"
Apache Kafka is an open-source software platform written in Scala and Java, mainly used for stream processing.
The use cases of Apache Kafka are:
Messaging
Website Activity Tracking
Metrics
Log Aggregation
Stream Processing
Event Sourcing
Commit Log
For more information use the official apache Kafka site.
https://kafka.apache.org/uses
Kafka is a pub-sub highly scalable messaging system. It acts as a transport layer guaranteeing exactly once semantics and Spark steaming does the processing. The next question that comes to my mind is even spark can poll directories to check for files and even read from a socket or port. How this Kafka and spark work in tandem ? I mean does an application written in some language instead of writing to a database for storage directly feds to the port (or places the files which would not really be tak time and would rather be some kind of batch processing) from which the data is then read by a Kafka producer and then via the Kafka consumer API is then read and processing by spark streaming?