Today, we will discuss the Advantages and Disadvantages of Kafka Because, it is very important to know the limitations of any technology before using it, same in case of advantages. So, let’s discuss Kafka Advantage and Disadvantage in detail.
2. Advantages of Kafka
So, here we are listing out some of the advantages of Kafka. Basically, these Kafka advantages are making Kafka ideal for our data lake implementation. So, let’s start learning advantages of Kafka in detail:
Kafka Pros and Cons – Kafka Advantages
a. High-throughput Without having not so large hardware, Kafka is capable of handling high-velocity and high-volume data. Also, able to support message throughput of thousands of messages per second. b. Low Latency It is capable of handling these messages with the very low latency of the range of milliseconds, demanded by most of the new use cases. c. Fault-Tolerant One of the best advantages is Fault Tolerance. There is an inherent capability in Kafka, to be resistant to node/machine failure within a cluster. d. Durability Here, durability refers to the persistence of data/messages on disk. Also, messages replication is one of the reasons behind durability, hence messages are never lost. e. Scalability Without incurring any downtime on the fly by adding additional nodes, Kafka can be scaled-out. Moreover, inside the Kafka cluster, the message handling is fully transparent and these are seamless. f. Distributed The distributed architecture of Kafka makes it scalable using capabilities like replication and partitioning. g. Message Broker Capabilities Kafka tends to work very well as a replacement for a more traditional message broker. Here, a message broker refers to an intermediary program, which translates messages from the formal messaging protocol of the publisher to the formal messaging protocol of the receiver. h. High Concurrency Kafka is able to handle thousands of messages per second and that too in low latency conditions with high throughput. In addition, it permits the reading and writing of messages into it at high concurrency. i. By Default Persistent As we discussed above that the messages are persistent, that makes it durable and reliable. j. Consumer Friendly It is possible to integrate with the variety of consumers using Kafka. The best part of Kafka is, it can behave or act differently according to the consumer, that it integrates with because each customer has a different ability to handle these messages, coming out of Kafka. Moreover, Kafka can integrate well with a variety of consumers written in a variety of languages. k. Batch Handling Capable (ETL like functionality) Kafka could also be employed for batch-like use cases and can also do the work of a traditional ETL, due to its capability of persists messages. l. Variety of Use Cases It is able to manage the variety of use cases commonly required for a Data Lake. For Example log aggregation, web activity tracking, and so on. m. Real-Time Handling Kafka can handle real-time data pipeline. Since we need to find a technology piece to handle real-time messages from applications, it is one of the core reasons for Kafka as our choice.
3. Disadvantages of Kafka
Cons of Kafka – Apache Kafka Disadvantages
It is good to know Kafka’s limitations even if its advantages appear more prominent then its disadvantages. However, consider it only when advantages are too compelling to omit. Here is one more condition that some disadvantages might be more relevant for a particular use case but not really linked to ours. So, here we are listing out some of the disadvantage associated with Kafka: a. No Complete Set of Monitoring Tools It is seen that it lacks a full set of management and monitoring tools. Hence, enterprise support staff felt anxious or fearful about choosing Kafka and supporting it in the long run. b. Issues with Message Tweaking As we know, the broker uses certain system calls to deliver messages to the consumer. However, Kafka’s performance reduces significantly if the message needs some tweaking. So, it can perform quite well if the message is unchanged because it uses the capabilities of the system. c. Not support wildcard topic selection There is an issue that Kafka only matches the exact topic name, that means it does not support wildcard topic selection. Because that makes it incapable of addressing certain use cases. d. Lack of Pace There can be a problem because of the lack of pace, while API’s which are needed by other languages are maintained by different individuals and corporates. e. Reduces Performance In general, there are no issues with the individual message size. However, the brokers and consumers start compressing these messages as the size increases. Due to this, when decompressed, the node memory gets slowly used. Also, compress happens when the data flow in the pipeline. It affects throughput and also performance. f. Behaves Clumsy Sometimes, it starts behaving a bit clumsy and slow, when the number of queues in a Kafka cluster increases. g. Lacks some Messaging Paradigms Some of the messaging paradigms are missing in Kafka like request/reply, point-to-point queues and so on. Not always but for certain use cases, it sounds problematic. So, this was all about the advantages and disadvantages of Kafka. Hope you like our explanation.
4. Conclusion: Advantages and Disadvantages of Kafka
Hence, we have seen all the Advantages and Disadvantages of Kafka in detail. That will help you a lot before using it. However, if any doubt occurs regarding Kafka Pros and Cons, feel free to ask through the comment section.
We use Apache Kafka when it comes to enabling communication between producers and consumers using message-based topics. Apache Kafka is a fast, scalable, fault-tolerant, publish-subscribe messaging system. Basically, it designs a platform for high-end new generation distributed applications. Also, it allows a large number of permanent or ad-hoc consumers. One of the best features of Kafka is, it is highly available and resilient to node failures and supports automatic recovery. This feature makes Apache Kafka ideal for communication and integration between components of large-scale data systems in real-world data systems.
Moreover, this technology replaces the conventional message brokers, with the ability to give higher throughput, reliability, and replication like JMS, AMQP and many more. In addition, core abstraction Kafka offers a Kafka broker, a Kafka Producer, and a Kafka Consumer. Kafka broker is a node on the Kafka cluster, its use is to persist and replicate the data. A Kafka Producer pushes the message into the message container called the Kafka Topic. Whereas a Kafka Consumer pulls the message from the Kafka Topic.
Before moving forward in Kafka Tutorial, let’s understand the actual meaning of term Messaging System in Kafka.
a. Messaging System in Kafka
When we transfer data from one application to another, we use the Messaging System. It results as, without worrying about how to share data, applications can focus on data only. On the concept of reliable message queuing, distributed messaging is based. Although, messages are asynchronously queued between client applications and messaging system. There are two types of messaging patterns available, i.e. point to point and publish-subscribe (pub-sub) messaging system. However, most of the messaging patterns follow pub-sub.
Point to Point Messaging System
Here, messages are persisted in a queue. Although, a particular message can be consumed by a maximum of one consumer only, even if one or more consumers can consume the messages in the queue. Also, it makes sure that as soon as a consumer reads a message in the queue, it disappears from that queue.
Publish-Subscribe Messaging System
Here, messages are persisted in a topic. In this system, Kafka Consumers can subscribe to one or more topic and consume all the messages in that topic. Moreover, message producers refer publishers and message consumers are subscribers here.
History of Apache Kafka
Previously, LinkedIn was facing the issue of low latency ingestion of huge amount of data from the website into a lambda architecture which could be able to process real-time events. As a solution, Apache Kafka was developed in the year 2010, since none of the solutions was available to deal with this drawback, before.
However, there were technologies available for batch processing, but the deployment details of those technologies were shared with the downstream users. Hence, while it comes to Real-time Processing, those technologies were not enough suitable. Then, in the year 2011 Kafka was made public.
Why Should we use Apache Kafka Cluster?
As we all know, there is an enormous volume of data in Big Data. And, when it comes to big data, there are two main challenges. One is to collect the large volume of data, while another one is to analyze the collected data. Hence, in order to overcome those challenges, we need a messaging system. Then Apache Kafka has proved its utility. There are numerous benefits of Apache Kafka such as:
Tracking web activities by storing/sending the events for real-time processes.
Alerting and reporting the operational metrics.
Transforming data into the standard format.
Continuous processing of streaming data to the topics.
Therefore, this technology is giving a tough competition to some of the most popular applications like ActiveMQ, RabbitMQ, AWS etc. because of its wide use.
Kafka Tutorial — Audience
Professionals who are aspiring to make a career in Big Data Analytics using Apache Kafka messaging system should refer this Kafka Tutorial article. It will give you complete understanding about Apache Kafka.
Kafka Tutorial — Prerequisites
You must have a good understanding ofJava, Scala, Distributed messaging system, and Linux environment, before proceeding with this Apache Kafka Tutorial.
Below we are discussing four core APIs in this Apache Kafka tutorial:
a. Kafka Producer API
This Kafka Producer API permits an application to publish a stream of records to one or more Kafka topics.
b. Kafka Consumer API
To subscribe to one or more topics and process the stream of records produced to them in an application, we use this Kafka Consumer API.
c. Kafka Streams API
In order to act as a stream processor consuming an input stream from one or more topics and producing an output stream to one or more output topics and also effectively transforming the input streams to output streams, this Kafka Streams API gives permission to an application.
d. Kafka Connector API
This Kafka Connector API allows building and running reusable producers or consumers that connect Kafka topics to existing applications or data systems. For example, a connector to a relational database might capture every change to a table.
Using the following components, Kafka achieves messaging:
a. Kafka Topic
Basically, how Kafka stores and organizes messages across its system and essentially a collection of messages are Topics. In addition, we can replicate and partition Topics. Here, replicate refers to copies and partition refers to the division. Also, visualize them as logs wherein, Kafka stores messages. However, this ability to replicate and partitioning topics is one of the factors that enable Kafka’s fault tolerance and scalability.
b. Kafka Producer
It publishes messages to a Kafka topic.
c. Kafka Consumer
This component subscribes to a topic(s), reads and processes messages from the topic(s).
d. Kafka Broker
Kafka Broker manages the storage of messages in the topic(s). If Kafka has more than one broker, that is what we call a Kafka cluster.
e. Kafka Zookeeper
To offer the brokers with metadata about the processes running in the system and to facilitate health checking and broker leadership election, Kafka uses Kafka zookeeper.
Kafka Tutorial — Log Anatomy
We view log as the partitions in this Kafka tutorial. Basically, a data source writes messages to the log. One of the advantages is, at any time one or more consumers read from the log they select. Here, the below diagram shows a log is being written by the data source and the log is being read by consumers at different offsets.
Kafka Tutorial — Data Log
By Kafka, messages are retained for a considerable amount of time. Also, consumers can read as per their convenience. However, if Kafka is configured to keep messages for 24 hours and a consumer is down for time greater than 24 hours, the consumer will lose messages. And, messages can be read from last known offset, if the downtime on part of the consumer is just 60 minutes. Kafka doesn’t keep state on what consumers are reading from a topic.
Kafka Tutorial — Partition in Kafka
There are few partitions in every Kafka broker. Moreover, each partition can be either a leader or a replica of a topic. In addition, along with updating of replicas with new data, Leader is responsible for all writes and reads to a topic. The replica takes over as the new leader if somehow the leader fails.
Importance of Java in Apache Kafka
Apache Kafka is written in pure Java and also Kafka’s native API is java. However, many other languages like C++, Python, .Net, Go, etc. also support Kafka. Still, a platform where there is no need of using a third-party library is Java. Also, we can say, writing code in languages apart from Java will be a little overhead.
In addition, we can useJavalanguage if we need the high processing rates that come standard on Kafka. Also, Java provides a good community support for Kafka consumer clients. Hence, it is a right choice to implement Kafka in Java.
For a more traditional message broker, Kafka works well as a replacement. We can say Kafka has better throughput, built-in partitioning, replication, and fault-tolerance which makes it a good solution for large-scale message processing applications.
For operational monitoring data, Kafka finds the good application. It includes aggregating statistics from distributed applications to produce centralized feeds of operational data.
Since it supports very large stored log data, that means Kafka is an excellent backend for applications of event sourcing.
Kafka Tutorial — Comparisons in Kafka
Many applications offer the same functionality as Kafka like ActiveMQ, RabbitMQ, Apache Flume, Storm, and Spark. Then why should you go for Apache Kafka instead of others?
Let’s see the comparisons below:
a. Apache Kafka vs Apache Flume
i. Types of tool
Apache Kafka– For multiple producers and consumers, it is a general-purpose tool.
Apache Flume– Whereas, it is a special-purpose tool for specific applications.
ii. Replication feature
Apache Kafka– Using ingest pipelines, it replicates the events.
Apache Flume- It does not replicate the events.
b. RabbitMQ vs Apache Kafka
One among the foremost Apache Kafka alternatives is RabbitMQ. So, let’s see how they differ from one another:
Apache Kafka– Basically, Kafka is distributed. Also, with guaranteed durability and availability, the data is shared and replicated.
RabbitMQ– It offers relatively less support for these features.
ii. Performance rate
Apache Kafka — Its performance rate is high to the tune of 100,000 messages/second.
RabbitMQ — Whereas, the performance rate of RabbitMQ is around 20,000 messages/second.
Apache Kafka — It allows reliable log distributed processing. Also, stream processing semantics built into the Kafka Streams.
RabbitMQ — Here, the consumer is just FIFO based, reading from the HEAD and processing 1 by 1.
c. Traditional queuing systems vs Apache Kafka
i. Messages Retaining
Traditional queuing systems — Most queueing systems remove the messages after it has been processed typically from the end of the queue.
Apache Kafka — Here, messages persist even after being processed. They don’t get removed as consumers receive them.
ii. Logic-based processing
Traditional queuing systems — It does not allow to process logic based on similar messages or events.
Apache Kafka — It allows to process logic based on similar messages or events.
So, this was all about Apache Kafka Tutorials. Hope you like our explanation.
Conclusion: Kafka Tutorial
Hence, in this Kafka Tutorial, we have seen the whole concept of Apache Kafka and seen what is Kafka. Moreover, we discussed Kafka components, use cases, and Kafka architecture. At last, we discussed the comparison of Kafka vs other messaging tools. Furthermore, if you have any query regarding Kafka Tutorial, feel free to ask in the comment section. Also, keep visiting Data Flair for more knowledgeable articles on Apache Kafka.