I read about lot of design stories where data reaches storage (both acid and non-sql) through Kafka. Not sure I understand in depth what case it solves. Why not directly?
On other hand, I've never seen other usages of Kafka. Is are other major case of?
Regards,
In short : coupling. If you write directly to your storage from your source system, you couple the two together. If you want to change one, you directly impact the other.
Kafka enables you to decouple this, and use data more effectively. Data in Kafka can be used by multiple independent consumers, so if you want to write it to multiple targets you still only extract it from the source system one.
This talk might help you understand further: "Embrace the Anarchy: Apache Kafka’s Role in Modern Data Architectures" Video / Slides
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We are planning to build a real time monitoring system with apache kafka. The overall idea is to push data from multiple data sources to kafka and perform data quality checks. I have few questions with this architecture
What are the best possible approaches of streaming data from multiple sources which mainly include java applications, oracle database, rest api's, log files to apache kafka? Note each client deployment includes each of such data sources. Hence the number of data sources pushing data to kafka would be equal to the number of customers * x where x are the types of data sources that I listed. Ideally a push approach would suit best instead of a pull approach. In the pull approach the target system would have to be configured with the credentials of various source system which would not be practical
How do we handle failures?
How do we perform data quality checks on the incoming messages? For e.g. If a certain message does not have all the required attributes, the message could be discarded and an alert could be raised for the maintenance team to check.
Kindly let me know your expert inputs. Thanks !
I think the best approach here is to use Kafka connect: link
but it's a pull approach :
Kafka Connect sources are pull-based for a few reasons. First, although connectors should generally run continuously, making them pull-based means that the connector/Kafka Connect decides when data is actually pulled, which allows for things like pausing connectors without losing data, brief periods of unavailability as connectors are moved, etc. Second, in distributed mode the tasks that pull data may need to be rebalanced across workers, which means they won't have a consistent location or address. While in standalone mode you could guarantee a fixed network endpoint to work with (and point other services at), this doesn't work in distributed mode where tasks can be moving around between workers. Ewen
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?
I am very much new to Kafka, and i am researching if Kafka can be used as a real time messaging broker rather than retaining and sending. In other words can it just do the basic pub/sub broker job without retaining at all.
Is it configurable in Kafka Server configurations?
I don't think it's possible to accomplish this. One of the key differences between Kafka and other messaging systems is that Kafka uses the underlying OS's to handle storage.
Another unconventional choice that we made is to avoid explicitly
caching messages in memory at the Kafka layer. Instead, we rely on
the underlying file system page cache. Whitepaper
So Kafka automatically writes messages to disk, so it retains them by default. This is a conscious decision the designers of Kafka have made that they believe is worth the tradeoffs.
If you're asking this because you're worried writing to disk may be slower than keeping things in memory.
We have found that both the production and the
consumption have consistent performance linear to the data size,
up to many terabytes of data. Whitepaper
So the size of the data that you've retained doesn't impact how fast the system is.
I have an application in production that has to process several gigabytes of messages per day. I like the Kafka architecture and performance a lot; it perfectly fits my needs.
I'd like to replace my messaging layer with Kafka at some point. Is the 0.7.1 version good enough for production use in terms of stability and consistency in performance?
It is definitely in use at several Big Data companies already, including LinkedIn, where it was created (and later open sourced), and Tumblr. Just Tumblr by itself handles many gigabytes of messages per day. I'm sure LinkedIn is way up there too. You can see a list of companies known to currently use it here:
https://cwiki.apache.org/confluence/display/KAFKA/Powered+By
Also, be sure to subscribe to their mailing list, there are lots of people actively trying it out and using it in production environments.
I'm sure it can handle whatever volume you can throw at it.
There is one critical feature I think Kafka is missing before it is ready for production.
"Flushing messages to disc if the producer can't reach any Kafka broker"
The issue has been filed a long time ago here:
https://issues.apache.org/jira/browse/KAFKA-156
This feature will makes the complete Kafka event pipline even more robust for some use-cases when the producer always has to be able to send events. For example when you track pageviews or like-button clicks and you don't want to miss any events, even if all Kafka brokers are unreachable.
I must agree with Dave, Kafka is a good tool but it missing some basic features which some can be done manually but then you need to think what Kafka provide. some missing things are:
(As Dave said) Flushing messages to disk when the producer fail to send them
Consumers ability to track which messages were handled (not just consumed) and which wasn't in case of a restart.
Monitoring - a way to receive the current status of the entities in the system like the current size of the queue in the producer or the write\read pace at the brokers (those can be done but are not part of the tool).
I have used kafka for quite sometime. Using native java and python clients would be preferred.
I had to struggle a lot finding a proper node.js client. literally re-wrote my whole code many a times using different clients as they had lot of bugs.
Finally settled with franz-kafka for node.js.
Apart from that maintaining the consumer offsets is a bit difficult. It is missing some good features like exchanges that exist in AMQP based Apache Qpid or RabbitMQ
Since it's distributed, supports offline messages and the performance is really impressive. I too preferred it :)