If the consumer is located in a different data center from the broker, you may require to tune the socket buffer size to amortize the long network latency.
Posted Date:- 2021-11-12 09:09:17
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, since the Producer doesn’t block.
Posted Date:- 2021-11-12 09:08:13
Simply, it implies that the Follower cannot fetch data as fast as data accumulated by the Leader.
Posted Date:- 2021-11-12 09:07:24
Kafka is not explicitly developed for Hadoop. Using it for writing and reading data is trickier than it is with Flume. However, Kafka is a highly reliable and scalable system used to connect multiple systems like Hadoop.
Posted Date:- 2021-11-12 09:06:24
This is because duplication assures that issued messages are absorbed in plan fault, appliance mistake or recurrent software promotions.
Posted Date:- 2021-11-12 09:05:43
A topic is a category or feed name to which records are published. Topics in Kafka are always multi-subscriber; that is, a topic can have zero, one, or many consumers that subscribe to the data written to it. For each topic, the Kafka cluster maintains a partitioned log.
Posted Date:- 2021-11-12 09:05:02
A topic is a category or feed name to which records are published. Topics in Kafka are always multi-subscriber; that is, a topic can have zero, one, or many consumers that subscribe to the data written to it. For each topic, the Kafka cluster maintains a partitioned log.
Posted Date:- 2021-11-12 09:04:11
To start a Kafka Server, the Zookeeper has to be powered up by using the following steps:
> bin/zookeeper-server-start.sh config/zookeeper.properties
> bin/kafka-server-start.sh config/server.properties
Posted Date:- 2021-11-12 09:03:35
Flume’s major use-case is to gulp down the data into Hadoop. The Flume is incorporated with the Hadoop’s monitoring system, file formats, file system and utilities such as Morphlines. Flume’s design of sinks, sources and channels mean that with the aid of Flume one can shift data among other systems lithely, but the main feature is its Hadoop integration.
The Flume is the best option used when you have non-relational data sources if you have a long file to stream into the Hadoop.
Kafka’s major use-case is a distributed publish-subscribe messaging system. Kafka is not developed specifically for Hadoop and using Kafka to read and write data to Hadoop is considerably trickier than it is in Flume.
Kafka can be used when you particularly need a highly reliable and scalable enterprise messaging system to connect many multiple systems like Hadoop.
Posted Date:- 2021-11-12 09:02:48
In Kafka, a cluster contains multiple brokers since it is a distributed system. Topic in the system will get divided into multiple partitions, and each broker stores one or more of those partitions so that multiple producers and consumers can publish and retrieve messages at the same time.
Posted Date:- 2021-11-12 09:02:01
It is impossible to use Kafka without ZooKeeper because it is not feasible to go around ZooKeeper and attach it in a straight line with the server. If ZooKeeper is down for a number of causes, then we will not be able to serve customers’ demands.
Posted Date:- 2021-11-12 09:00:47
QueueFullException naturally happens when the manufacturer tries to propel communications at a speed which a broker can’t grip. Consumers need to insert sufficient brokers to collectively grip the amplified load since the producer doesn’t block.
Posted Date:- 2021-11-12 09:00:06
It is responsible for covering two producers: kafka.producer.SyncProducer and kafka.producer.async.AsyncProducer. Kafka Producer API mainly provides all producer performance to its clients through a single API.
Posted Date:- 2021-11-12 08:58:26
Duplication assures that the issued messages available are absorbed in the case of any appliance mistake, plan fault, or recurrent software promotions.
Posted Date:- 2021-11-12 08:57:34
Partitions of a topic are distributed across servers in a Kafka cluster. Each server handles the data and requests with its share of partitions. Partitions can be replicated across multiple servers to ensure fault tolerance. Every partition has one server that plays the role of a leader for that partition. The leader handles all the read and writes requests for that particular partition. A leader can have zero or more followers. The followers passively replicate the leader. In the case where the leader fails, one of the followers can take on the role of the leader.
Posted Date:- 2021-11-12 08:57:01
Kafka acts as the central nervous system that makes streaming data available to applications. It builds real-time data pipelines responsible for data processing and transferring between different systems that need to use it.
Posted Date:- 2021-11-12 08:56:14
Partitions are created in Kafka based on consumer groups and offset. One server in the partition serves as the leader, and one or more servers act as a follower. The leader assigns itself tasks that read and write partition requests. Followers follow the leader and replicate what is being told.
Posted Date:- 2021-11-12 08:53:53
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.
Posted Date:- 2021-11-12 08:53:20
To get precisely one messaging from data production, you have to follow two things avoiding duplicates during data production and avoiding duplicates during data consumption. For this, include a primary key in the message and de-duplicate on the consumer.
Posted Date:- 2021-11-12 08:52:04
The Kafka MirrorMaker provides Geo-replication support for clusters. The messages are replicated across multiple cloud regions or datacenters. This can be used in passive/active scenarios for recovery and backup.
Posted Date:- 2021-11-12 08:51:27
Kafka ensures more durability and is scalable even though both are used for real-time processing.
Posted Date:- 2021-11-12 08:50:51
The traditional method of message transfer includes two methods
• Queuing: In a queuing, a pool of consumers may read message from the server and each message goes to one of them
• Publish-Subscribe: In this model, messages are broadcasted to all consumers
Kafka caters single consumer abstraction that generalized both of the above- the consumer group.
Posted Date:- 2021-11-12 08:48:52
Kafka cluster is basically a group of multiple brokers. They are used to maintain load balance. Because Kafka brokers are stateless, they rely on Zookeeper to keep track of their cluster state. A single Kafka broker instance can manage hundreds of thousands of reads and writes per second, and each broker can handle TBs of messages without compromising performance. Zookeeper can be used to choose the Kafka broker leader. Thus having a cluster of Kafka brokers heavily increases the performance.
Posted Date:- 2021-11-12 08:47:36
Geo-Replication is a Kafka feature that allows messages in one cluster to be copied across many data centers or cloud regions. Geo-replication entails replicating all of the files and storing them throughout the globe if necessary. Geo-replication can be accomplished with Kafka's MirrorMaker Tool. Geo-replication is a technique for ensuring data backup.
Posted Date:- 2021-11-12 08:46:54
A replica that has been out of ISR for a long period of time indicates that the follower is unable to fetch data at the same rate as the leader.
Posted Date:- 2021-11-12 08:46:26
By default, the maximum size of a Kafka message is 1MB (megabyte). The broker settings allow you to modify the size. Kafka, on the other hand, is designed to handle 1KB messages as well.
Posted Date:- 2021-11-12 08:45:59
Apache Kafka has 4 main APIs:
1. Producer API
2. Consumer API
3. Streams API
4. Connector API
Posted Date:- 2021-11-12 08:45:16
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.
Posted Date:- 2021-11-12 08:44:35
Even though both are used for real-time processing, Kafka is scalable and ensures message durability.
These are some of the frequently asked Apache Kafka interview questions with answers. You can brush up on your knowledge of Apache Kafka with these blogs.
Posted Date:- 2021-11-12 08:42:59
The role of Kafka’s Producer API is to wrap the two producers – kafka.producer.SyncProducer and the kafka.producer.async.AsyncProducer. The goal is to expose all the producer functionality through a single API to the client.
Posted Date:- 2021-11-12 08:42:32
QueueFullException typically occurs when the Producer attempts to send messages at a pace that the Broker cannot handle. Since the Producer doesn’t block, users will need to add enough brokers to collaboratively handle the increased load.
Posted Date:- 2021-11-12 08:42:06
Within the Producer, the role of a Partitioning Key is to indicate the destination partition of the message. By default, a hashing-based Partitioner is used to determine the partition ID given the key. Alternatively, users can also use customized Partitions.
Posted Date:- 2021-11-12 08:41:44
Since Kafka uses ZooKeeper, it is essential to initialize the ZooKeeper server, and then fire up the Kafka server.
Posted Date:- 2021-11-12 08:41:25
It means that the Follower is unable to fetch data as fast as data accumulated by the Leader.
Posted Date:- 2021-11-12 08:40:10
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.
Posted Date:- 2021-11-12 08:39:55
Replication ensures that published messages are not lost and can be consumed in the event of any machine error, program error or frequent software upgrades.
Posted Date:- 2021-11-12 08:38:50
Replicas are essentially a list of nodes that replicate the log for a particular partition irrespective of whether they play the role of the Leader. On the other hand, ISR stands for In-Sync Replicas. It is essentially a set of message replicas that are synced to the leaders.
Posted Date:- 2021-11-12 08:38:21
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.
Posted Date:- 2021-11-12 08:37:07
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.
Posted Date:- 2021-11-12 08:36:39
Apache ZooKeeper is a naming registry for distributed applications as well as a distributed, open-source configuration and synchronization service. It keeps track of the Kafka cluster nodes' status, as well as Kafka topics, partitions, and so on.
ZooKeeper is used by Kafka brokers to maintain and coordinate the Kafka cluster. When the topology of the Kafka cluster changes, such as when brokers and topics are added or removed, ZooKeeper notifies all nodes. When a new broker enters the cluster, for example, ZooKeeper notifies the cluster, as well as when a broker fails. ZooKeeper also allows brokers and topic partition pairs to elect leaders, allowing them to select which broker will be the leader for a given partition (and server read and write operations from producers and consumers), as well as which brokers contain clones of the same data. When the cluster of brokers receives a notification from ZooKeeper, they immediately begin to coordinate with one another and elect any new partition leaders that are required. This safeguards against the unexpected absence of a broker.
Posted Date:- 2021-11-12 08:35:46
Kafka topics are separated into partitions, each of which contains records in a fixed order. A unique offset is assigned and attributed to each record in a partition. Multiple partition logs can be found in a single topic. This allows several users to read from the same topic at the same time. Topics can be parallelized via partitions, which split data into a single topic among numerous brokers.
Replication in Kafka is done at the partition level. A replica is the redundant element of a topic partition. Each partition often contains one or more replicas, which means that partitions contain messages that are duplicated across many Kafka brokers in the cluster.
One server serves as the leader of each partition (replica), while the others function as followers. The leader replica is in charge of all read-write requests for the partition, while the followers replicate the leader. If the lead server goes down, one of the followers takes over as the leader. To disperse the burden, we should aim for a good balance of leaders, with each broker leading an equal number of partitions.
Posted Date:- 2021-11-12 08:35:31
Following are the four core APIs that Kafka uses:
* Producer API:
The Producer API in Kafka allows an application to publish a stream of records to one or more Kafka topics.
* Consumer API:
An application can subscribe to one or more Kafka topics using the Kafka Consumer API. It also enables the application to process streams of records generated in relation to such topics.
* Streams API:
The Kafka Streams API allows an application to use a stream processing architecture to process data in Kafka. An application can use this API to take input streams from one or more topics, process them using streams operations, and generate output streams to transmit to one or more topics. The Streams API allows you to convert input streams into output streams in this manner.
* Connect API:
The Kafka Connector API connects Kafka topics to applications. This opens up possibilities for constructing and managing the operations of producers and consumers, as well as establishing reusable links between these solutions. A connector, for example, may capture all database updates and ensure that they are made available in a Kafka topic.
Posted Date:- 2021-11-12 08:34:58
Following are the key features of Kafka:-
* Kafka is a messaging system built for high throughput and fault tolerance.
* Kafka has a built-in patriation system known as a Topic.
* Kafka Includes a replication feature as well.
* Kafka provides a queue that can handle large amounts of data and move * messages from one sender to another.
* Kafka can also save the messages to storage and replicate them across the cluster.
* For coordination and synchronization with other services, Kafka collaborates with Zookeeper.
* Apache Spark is well supported by Kafka.
Posted Date:- 2021-11-12 08:34:07
Every partition in Kafka has one server which plays the role of a Leader, and none or more servers that act as Followers. The Leader performs the task of all read and write requests for the partition, while the role of the Followers is to passively replicate the leader. In the event of the Leader failing, one of the Followers will take on the role of the Leader. This ensures load balancing of the server.
Posted Date:- 2021-11-12 08:33:05
No, it is not possible to bypass Zookeeper and connect directly to the Kafka server. If, for some reason, ZooKeeper is down, you cannot service any client request.
Posted Date:- 2021-11-12 08:32:46
Kafka uses Zookeeper to store offsets of messages consumed for a specific topic and partition by a specific Consumer Group.
Posted Date:- 2021-11-12 08:32:30
Consumer Groups is a concept exclusive to Kafka. Every Kafka consumer group consists of one or more consumers that jointly consume a set of subscribed topics.
Posted Date:- 2021-11-12 08:31:55
Messages contained in the partitions are assigned a unique ID number that is called the offset. The role of the offset is to uniquely identify every message within the partition.
Posted Date:- 2021-11-12 08:31:42
The four major components of Kafka are:
* Topic – a stream of messages belonging to the same type
* Producer – that can publish messages to a topic
* Brokers – a set of servers where the publishes messages are stored
* Consumer – that subscribes to various topics and pulls data from the brokers.
Posted Date:- 2021-11-12 08:31:07
Apache Kafka is a streaming platform that is free and open-source. Kafka was first built at LinkedIn as a messaging queue, but it has evolved into much more. It's a versatile tool for working with data streams that may be applied to a variety of scenarios.
Kafka is a distributed system, which means it can scale up as needed. All you have to do now is add new Kafka nodes (servers) to the cluster.
Kafka can process a large amount of data in a short amount of time. It also has low latency, making it possible to process data in real-time. Although Apache Kafka is written in Scala and Java, it may be used with a variety of different programming languages.
Traditional message queues, like RabbitMQ, are not the same as Kafka. RabbitMQ eliminates messages immediately after the consumer confirms them, whereas Kafka keeps them for a period of time (default is 7 days) after they've been received. RabbitMQ also sends messages to consumers and monitors their load. It determines how many messages each consumer should be processing at any one time. On the other hand, Consumers can retrieve messages from Kafka by pulling. It is built to be scalable horizontally by adding more nodes.
It is used for fault-tolerant storage as well as publishing and subscribing to a stream of records. The programs are intended to process timing and consumption records. Kafka replicates log partitions from many hosts. Developers and users contribute coding updates, which it keeps, reads, and analyses in real-time. For messaging, website activity tracking, log aggregation, and commit logs, Kafka is employed. Although Kafka can be used as a database, it lacks a data schema and indexes.
Posted Date:- 2021-11-12 08:30:31