Apache Kafka is a publish-subscribe open source message broker application. This messaging application was coded in “Scala”. Basically, this project was started by the Apache software. Kafka’s design pattern is mainly based on the transactional logs design.
For detailed understanding of Kafka, go through,
Kafka Tutorial.
The most important elements of Kafka are:
- Topic – Kafka Topic is the bunch or a collection of messages.
- Producer – In Kafka, Producers issue communications as well as publishes messages to a Kafka topic.
- Consumer – Kafka Consumers subscribes to a topic(s) and also reads and processes messages from the topic(s).
- Brokers –While it comes to manage storage of messages in the topic(s) we use Kafka Brokers.
For detailed understanding of Kafka components, go through,
Kafka – Architecture
Ans. There is a sequential ID number given to the messages in the partitions what we call, an offset. So, to identify each message in the partition uniquely, we use these offsets.
Ans. The concept of Consumer Groups is exclusive to Apache Kafka. Basically, every Kafka consumer group consists of one or more consumers that jointly consume a set of subscribed topics.
Ans. Apache Kafka is a distributed system is built to use Zookeeper. Although, Zookeeper’s main role here is to build coordination between different nodes in a cluster. However, we also use Zookeeper to recover from previously committed offset if any node fails because it works as periodically commit offset.
Ans. It is impossible to bypass Zookeeper and connect directly to the Kafka server, so the answer is no. If somehow, ZooKeeper is down, then it is impossible to service any client request.
Ans. In every Kafka broker, there are few partitions available. And, here each partition in Kafka can be either a leader or a replica of a topic. https://stackoverflow.com/questions/49054946/kafka-topic-partitions
Ans. There are some advantages of Kafka, which makes it significant to use:
- High-throughput : We do not need any large hardware in Kafka, because it is capable of handling high-velocity and high-volume data. Moreover, it can also support message throughput of thousands of messages per second.
- Low Latency : Kafka can easily handle these messages with the very low latency of the range of milliseconds, demanded by most of the new use cases.
- Fault-Tolerant : Kafka is resistant to node/machine failure within a cluster.
- Durability : As Kafka supports messages replication, so, messages are never lost. It is one of the reasons behind durability.
- Scalability : Kafka can be scaled-out, without incurring any downtime on the fly by adding additional nodes.
Kafka – Pros & Cons
Ans. Apache Kafka has 4 main APIs:
- Producer API
- Consumer API
- Streams API
- Connector API
Ans. Mainly, Kafka Consumer subscribes to a topic(s), and also reads and processes messages from the topic(s). Moreover, with a consumer group name, Consumers label themselves. In other words, within each subscribing consumer group, each record published to a topic is delivered to one consumer instance. Make sure it is possible that Consumer instances can be in separate processes or on separate machines.
Ans. In every partition of Kafka, there is one server which acts as the Leader, and none or more servers plays the role as a Followers.
Ans. As the main role of the Leader is to perform the task of all read and write requests for the partition, whereas Followers passively replicate the leader. Hence, at the time of Leader failing, one of the Followers takeover the role of the Leader. Basically, this entire process ensures load balancing of the servers.
Ans. Basically, a list of nodes that replicate the log is Replicas. Especially, for a particular partition. However, they are irrespective of whether they play the role of the Leader. In addition, ISR refers to In-Sync Replicas. On defining ISR, it is a set of message replicas that are synced to the leaders.
Ans. Because of Replication, we can be sure that published messages are not lost and can be consumed in the event of any machine error, program error or frequent software upgrades.
Ans. Simply, it implies that the Follower cannot fetch data as fast as data accumulated by the Leader.
Ans. It is the very important step to initialize the ZooKeeper server because Kafka uses ZooKeeper.So, the process for starting a Kafka server is:
In order to start the ZooKeeper server: > bin/zookeeper-server-start.sh config/zookeeper.properties
Next, to start the Kafka server: > bin/kafka-server-start.sh config/server.properties
Ans. whenever the Kafka Producer attempts to send messages at a pace that the Broker cannot handle at that time QueueFullException typically occurs. However, to collaboratively handle the increased load, users will need to add enough brokers(servers, nodes), since the Producer doesn’t block.
Kafka Broker
Ans. An API which permits an application to publish a stream of records to one or more Kafka topics is what we call Producer API.
Ans. The main difference between Kafka and Flume are: Types of tool
- Apache Kafka– As Kafka is a general-purpose tool for both multiple producers and consumers.
- Apache Flume– Whereas, Flume is considered as a special-purpose tool for specific applications.
Replication feature
- Apache Kafka– Kafka can replicate the events.
- Apache Flume- whereas, Flume does not replicate the events.
Ans. Undoubtedly, Kafka is a streaming platform. It can help: To push records easily. Also, can store a lot of records without giving any storage problems Moreover, it can process the records as they come in
Ans. It can perform in several ways, such as:
- In order to transmit data between two systems, we can build a real-time stream of data pipelines with it.
- Also, we can build a real-time streaming platform with Kafka, that can actually react to the data.
Ans. However, retention period retains all the published records within the Kafka cluster. It doesn’t check whether they have been consumed or not. Moreover, the records can be discarded by using a configuration setting for the retention period. And, it results as it can free up some space.
Ans. The maximum size of a message that can be received by the Kafka is approx. 1000000 bytes.
Ans. Basically, there are two methods of the traditional message transfer method, such as:
- Queuing: It is a method in which a pool of consumers may read a message from the server and each message goes to one of them.
- Publish-Subscribe: Whereas in Publish-Subscribe, messages are broadcasted to all consumers.
Ans. ISR refers to In sync replicas. These are generally classified as a set of message replicas which are synced to be leaders.
Ans. For our cluster, Kafka MirrorMaker offers geo-replication. Basically, messages are replicated across multiple data centers or cloud regions, with MirrorMaker. So, it can be used in active/passive scenarios for backup and recovery; or also to place data closer to our users, or support data locality requirements.
Ans. We can easily deploy Kafka as a multi-tenant solution. However, by configuring which topics can produce or consume data, Multi-tenancy is enabled. Also, it provides operations support for quotas.
https://softwareengineering.stackexchange.com/questions/351524/what-exactly-is-a-multi-tenant-application
Ans. An API which permits an application to subscribe to one or more topics and also to process the stream of records produced to them is what we call Consumer API.
Ans. An API which permits an application to act as a stream processor, and also consuming an input stream from one or more topics and producing an output stream to one or more output topics, moreover, transforming the input streams to output streams effectively, is what we call Streams API.
Ans. An API which permits to run as well as build the reusable producers or consumers which connect Kafka topics to existing applications or data systems is what we call the Connector API.
Ans. The main role of Producers is to publish data to the topics of their choice. Basically, its duty is to select the record to assign to partition within the topic.
Ans. One of the Apache Kafka’s alternative is RabbitMQ. So, let’s compare both:
- Features
- Apache Kafka– Kafka is distributed, durable and highly available, here the data is shared as well as replicated.
- RabbitMQ– There are no such features in RabbitMQ.
- Performance rate
- Apache Kafka– To the tune of 100,000 messages/second.
- RabbitMQ- In case of RabbitMQ, the performance rate is around 20,000 messages/second.
Ans. Let’s compare Traditional queuing systems vs Apache Kafka feature-wise: Messages Retaining
- Traditional queuing systems– It deletes the messages just after processing completion typically from the end of the queue.
- Apache Kafka– But in Kafka, messages persist even after being processed. That implies messages in Kafka don’t get removed as consumers receive them. Logic-based processing
- Traditional queuing systems–Traditional queuing systems don’t permit to process logic based on similar messages or events.
- Apache Kafka– Kafka permits to process logic based on similar messages or events.
Ans. In order to overcome the challenges of collecting the large volume of data, and analyzing the collected data we need a messaging system. Hence Apache Kafka came in the story. Its benefits are:
- It is possible to track web activities just by storing/sending the events for real-time processes.
- Through this, we can Alert as well as report the operational metrics.
- Also, we can transform data into the standard format. *Moreover, it allows continuous processing of streaming data to the topics.
- Due to its this wide use, it is ruling over some of the most popular applications like ActiveMQ, RabbitMQ, AWS etc.
Ans. We view log as the partitions. 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.
Ans. As we know, messages are retained for a considerable amount of time in Kafka. Moreover, there is flexibility for consumers that they can read as per their convenience. Although, there is a possible case that if Kafka is configured to keep messages for 24 hours and possibly that time consumer is down for time greater than 24 hours, then the consumer may lose those messages. However, still, we can read those messages from last known offset, but only at a condition that the downtime on part of the consumer is just 60 minutes. Moreover, on what consumers are reading from a topic Kafka doesn’t keep state.
Ans. So, ways to tune Apache Kafka it is to tune its several components:
- Tuning Kafka Producers
- Kafka Brokers Tuning
- Tuning Kafka Consumers Ways for Kafka Optimization
Ans. Limitations of Kafka are:
- No Complete Set of Monitoring Tools
- Issues with Message Tweaking
- Not support wildcard topic selection
- Lack of Pace
Kafka – Pros & Cons
Ans. Apache Kafka Operations are:
- Addition and Deletion of Kafka Topics
- How to modify the Kafka Topics
- Distinguished Turnoff
- Mirroring Data between Kafka Clusters
- Finding the position of the Consumer
- Expanding Your Kafka Cluster
- Migration of Data Automatically
- Retiring Servers
- Datacenters Kafka – Operations
Ans. Apache Kafka has so many use cases, such as:
- Kafka Metrics It is possible to use Kafka for operational monitoring data. Also, to produce centralized feeds of operational data, it involves aggregating statistics from distributed applications.
- Kafka Log Aggregation Moreover, to gather logs from multiple services across an organization.
- Stream Processing While stream processing, Kafka’s strong durability is very useful.
Ans. Some of the real-time applications are:
- Netflix
- Mozilla
- Oracle
Kafka – Applications
Ans. Some best features of Kafka Stream are
- Kafka Streams are highly scalable and fault-tolerant.
- Kafka deploys to containers, VMs, bare metal, cloud.
- We can say, Kafka streams are equally viable for small, medium, & large use cases.
- Also, it is fully in integration with Kafka security.
- Write standard Java applications.
- Exactly-once processing semantics.
- Moreover, there is no need of separate processing cluster.
Ans. The type of processing of data continuously, real-time, concurrently, and in a record-by-record fashion is what we call Kafka Stream processing.
Ans. There are three types of System tools:
- Kafka Migration Tool It helps to migrate a broker from one version to another.
- Mirror Maker Mirror Maker tool helps to offer to mirror of one Kafka cluster to another.
- Consumer Offset Checker For the specified set of Topics as well as Consumer Group, it shows Topic, Partitions, Owner.
Ans. For the purpose of stronger durability and higher availability, replication tool is available here. Its types are −
- Create Topic Tool
- List Topic Tool
- Add Partition Tool
Ans. For the need of the high processing rates that come standard on Kafka, we can use java language. Moreover, for Kafka consumer clients also, Java offers a good community support. So, we can say it is a right choice to implement Kafka in Java.
Ans. The best feature of Kafka is “Variety of Use Cases”. It means Kafka is able to manage the variety of use cases which are very common for a Data Lake. For Example log aggregation, web activity tracking, and so on.
Ans. It is very important to factor in topic replication while designing a Kafka system. Hence, if in any case, broker goes down its topics’ replicas from another broker can solve the crisis.
Ans. So, the use cases are:
- The New York Times: This company uses it to store and distribute, in real-time, published content to the various applications and systems that make it available to the readers. Basically, it uses Apache Kafka and the Kafka Streams both.
- Zalando: As an ESB (Enterprise Service Bus) as the leading online fashion retailer in Europe Zalando uses Kafka.
- LINE: Basically, to communicate to one another LINE application uses Apache Kafka as a central data hub for their services.
Ans. They are:
- The order will be same for both the Messages sent by a producer to a particular topic partition. That
- Moreover, the consumer instance sees records in the order in which they are stored in the log.
- Also, we can tolerate up to N-1 server failures, even without losing any records committed to the log.
Kafka Tutorials
Reference: Interview Questions