Showing posts with label leader. Show all posts
Showing posts with label leader. Show all posts

Monday, 3 January 2022

Top 20 Apache Kafka Interview Questions and Answers


Apache Kafka is a free and open-source streaming platform. Kafka began as a messaging queue at LinkedIn, but it has since grown into much more. It's a flexible tool for working with data streams that may be used in a wide range of situations. Because Kafka is a distributed system, it can scale up and down as needed. All that's left to do now is expand the cluster with new Kafka nodes (servers).

In a short length of time, Kafka can process a big volume of data. It also has a low latency, allowing for real-time data processing. Despite the fact that Apache Kafka is written in Scala and Java, it may be utilised with a wide range of computer languages.

 Apache Hive Interview Questions & Answers

Ques. 1): What exactly do you mean when you say "confluent kafka"? What are the benefits?


Confluent is an Apache Kafka-based data streaming platform that can do more than just publish and subscribe. It can also store and process data within the stream. Confluent Kafka is a more extensive version of Apache Kafka. It improves Kafka's integration capabilities by adding tools for optimising and maintaining Kafka clusters, as well as methods for ensuring the security of the streams. Because of the Confluent Platform, Kafka is simple to set up and use. Confluent's software is available in three flavours:

A free, open-source streaming platform that makes working with real-time data streams a breeze;

A premium cloud-based version with more administration, operations, and monitoring features; an enterprise-grade version with more administration, operations, and monitoring tools.

Following are the advantages of Confluent Kafka :

  • It features practically all of Kafka's characteristics, as well as a few extras.
  • It greatly simplifies the administrative operations procedures.
  • It relieves data managers of the burden of thinking about data relaying.

 Apache Ambari interview Questions & Answers

Ques. 2): What are some of Kafka's characteristics?


The following are some of Kafka's most notable characteristics:-

  • Kafka is a fault-tolerant messaging system with a high throughput.
  • A Topic is a built-in patriation system in Kafka.
  • Kafka also comes with a replication mechanism.
  • Kafka is a distributed messaging system that can manage massive volumes of data and transfer messages from one sender to another.
  • The messages can also be saved to storage and replicated across the cluster using Kafka.
  • Kafka works with Zookeeper for synchronisation and collaboration with other services.
  • Kafka provides excellent support for Apache Spark.

 Apache Tapestry Interview Questions and Answers

Ques. 3): What are some of the real-world usages of Apache Kafka?


The following are some examples of Apache Kafka's real-world applications:

Message Broker: Because Apache Kafka has a high throughput value, it can handle a large number of similar sorts of messages or data. Apache Kafka can be used as a publish-subscribe messaging system that makes it simple to read and publish data.

To keep track of website activity, Apache Kafka can check if data is successfully delivered and received by websites. Apache Kafka is capable of handling the huge volumes of data generated by websites for each page as well as user actions.

To keep track of metrics connected to certain technologies, such as security logs, we can utilise Apache Kafka to monitor operational data.

Data logging: Apache Kafka provides data replication between nodes functionality that can be used to restore data on failed nodes. It can also be used to collect data from various logs and make it available to consumers.

Stream Processing with Kafka: Apache Kafka can also handle streaming data, the data that is read from one topic, processed, and then written to another. Users and applications will have access to a new topic containing the processed data.

 Apache NiFi Interview Questions & Answers

Ques. 4): What are some of Kafka's disadvantages?


The following are some of Kafka's drawbacks:

  • When messages are tweaked, Kafka performance suffers. Kafka works well when the message does not need to be updated.
  • Kafka does not support wildcard topic selection. It's crucial to use the appropriate issue name.
  • When dealing with large messages, brokers and consumers degrade Kafka's performance by compressing and decompressing the messages. This has an effect on Kafka's performance and throughput.
  • Kafka does not support several message paradigms, such as point-to-point queues and request/reply.
  • Kafka lacks a comprehensive set of monitoring tools.

 Apache Spark Interview Questions & Answers

Ques. 5): What are the use cases of Kafka monitoring?


The following are some examples of Kafka monitoring use cases:

  • Monitor the use of system resources: It can be used to track the usage of system resources like memory, CPU, and disc over time.
  • Threads and JVM consumption should be monitored: To free up memory, Kafka relies on the Java garbage collector, which ensures that it runs frequently, ensuring that the Kafka cluster is more active.
  • Maintain an eye on the broker, controller, and replication statistics so that partition and replica statuses can be changed as needed.
  • Identifying which applications are producing excessive demand and performance bottlenecks may aid in quickly resolving performance issues.


Ques. 6): What is the difference between Kafka and Flume?


Flume's main application is ingesting data into Hadoop. Hadoop's monitoring system, file types, file system, and tools like Morphlines are all incorporated into the Flume. When working with non-relational data sources or streaming a huge file into Hadoop, the Flume is the best option.

Kafka's main use case is as a distributed publish-subscribe messaging system. Kafka was not created with Hadoop in mind, therefore using it to gather and analyse data for Hadoop is significantly more difficult than using Flume.

When a highly reliable and scalable corporate communications system, such as Hadoop, is required, Kafka can be used.


Ques. 7): Explain the terms "leader" and "follower."


In Kafka, each partition has one server that acts as a Leader and one or more servers that operate as Followers. The Leader is in charge of all read and write requests for the partition, while the Followers are responsible for passively replicating the leader. In the case that the Leader fails, one of the Followers will assume leadership. The server's load is balanced as a result of this.


Ques. 8): What are the traditional methods of message transfer? How is Kafka better from them?


The classic techniques of message transmission are as follows: -

Message Queuing: -

The message queuing pattern employs a point-to-point approach. A message in the queue will be discarded once it has been eaten, similar to how a message in the Post Office Protocol is removed from the server once it has been delivered. These queues allow for asynchronous messaging.

If a network difficulty prevents a message from being delivered, such as when a consumer is unavailable, the message will be queued until it is transmitted. As a result, messages aren't always sent in the same order. Instead, they are distributed on a first-come, first-served basis, which in some cases can improve efficiency.

Publisher - Subscriber Model:-

The publish-subscribe pattern entails publishers producing ("publishing") messages in multiple categories and subscribers consuming published messages from the various categories to which they are subscribed. Unlike point-to-point texting, a message is only removed once it has been consumed by all category subscribers.

Kafka caters to a single consumer abstraction, the consumer group, which contains both of the aforementioned. The advantages of adopting Kafka over standard communications transfer mechanisms are as follows:

Scalable: Data is partitioned and streamlined using a cluster of devices, which increases storage capacity.

Faster: A single Kafka broker can handle megabytes of reads and writes per second, allowing it to serve thousands of customers.

Durability and Fault-Tolerant: The data is kept persistent and tolerant to any hardware failures by copying the data in the clusters.


Ques. 9): What is a Replication Tool in Kafka? Explain how to use some of Kafka's replication tools.


The Kafka Replication Tool is used to define the replica management process at a high level. Some of the replication tools available are as follows:

Replica Leader Election Tool of Choice: The Preferred Replica Leader Election Tool distributes partitions to many brokers in a cluster, each of which is known as a replica. The favourite replica is a term used to describe the leader. For various partitions, the brokers generally distribute the leader position fairly across the cluster, but due to failures, planned shutdowns, and other circumstances, an imbalance might develop over time. By reassigning the preferred copies, and hence the leaders, this tool can be utilised to maintain the balance in these instances.

Topics tool: The Kafka topics tool is in charge of all administration operations relating to topics, including:

  • Listing and describing the topics.
  • Topic generation.
  • Modifying Topics.
  • Adding a topic's dividers.
  • Disposing of topics.

Tool to reassign partitions: The replicas assigned to a partition can be changed with this tool. This refers to adding or removing followers from a partition.

StateChangeLogMerger tool: The StateChangeLogMerger tool collects data from brokers in a cluster, formats it into a central log, and aids in the troubleshooting of state change issues. Sometimes there are issues with the election of a leader for a particular partition. This tool can be used to figure out what's causing the issue.

Change topic configuration tool: used to create new configuration choices, modify current configuration options, and delete configuration options.


Ques. 10):  Explain the four core API architecture that Kafka uses.


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:

The Kafka Consumer API allows an application to subscribe to one or more Kafka topics. It also allows the programme to handle streams of records generated in connection with such topics.

Streams API: The Kafka Streams API allows an application to process data in Kafka using a stream processing architecture. This API allows an application to take input streams from one or more topics, process them with streams operations, and then generate output streams to send to one or more topics. In this way, the Streams API allows you to turn input streams into output streams.

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.


Ques. 11): Is it possible to utilise Kafka without Zookeeper?


As of version 2.8, Kafka can now be utilised without ZooKeeper. When Kafka 2.8.0 was released in April 2021, we all had the opportunity to check it out without ZooKeeper. This version, however, is not yet ready for production and is missing a few crucial features.

It was not feasible to connect directly to the Kafka broker without using Zookeeper in prior versions. This is because the Zookeeper is unable to fulfil client requests when it is down.


Ques. 12): Explain Kafka's concept of leader and follower.


Each partition in Kafka has one server acting as a Leader and one or more servers acting as Followers. The Leader is in control of the partition's read and write requests, while the Followers are in charge of passively replicating the leader. If the Leader is unable to lead, one of the Followers will take over. As a result, the server's load is balanced.


Ques. 13): In Kafka, what is the function of partitions?


From the standpoint of the Kafka broker, partitions allow a single topic to be partitioned across many servers. This gives you the ability to store more data in a single topic than a single server. If you have three brokers and need to store 10TB of data in a topic, you can create a subject with only one partition and store the entire 10TB on one broker. Another option is to create a three-partitioned topic with 10 TB of data distributed across all brokers. From the consumer's perspective, a partition is a unit of parallelism.


Ques. 14): In Kafka, what do you mean by geo-replication?


Geo-replication is a feature in Kafka that allows you to copy messages from one cluster to a number of other data centres or cloud locations. You can use geo-replication to replicate all of the files and store them all over the world if necessary. Using Kafka's MirrorMaker Tool, we can achieve geo-replication. We can ensure data backup without fail by employing the geo-replication strategy.


Ques. 15): Is Apache Kafka a platform for distributed streaming? What are you going to do with it?


Yes. Apache Kafka is a platform for distributed streaming data. Three critical capabilities are included in a streaming platform:

  • We can easily push records using a distributed streaming infrastructure.
  • It has a large storage capacity and allows us to store a large number of records without difficulty.
  • It assists us in processing records as they arrive.
  • The Kafka technology allows us to do the following:
  • We may create a real-time stream of data pipelines using Apache Kafka to send data between two systems.
  • We could also create a real-time streaming platform that reacts to data.


Ques. 16): What is Apache Kafka Cluster used for?


Apache Kafka Cluster is a messaging system that is used to overcome the challenges of gathering and processing enormous amounts of data. The following are the most important advantages of Apache Kafka Cluster:

We can track web activities using Apache Kafka Cluster by storing/sending events for real-time processes.

We may use this to both alert and report on operational metrics.

We can also use Apache Kafka Cluster to transform data into a common format.

It enables the processing of streaming data to the subjects in real time.

It is currently ruling over some of the most popular programmes such as ActiveMQ, RabbitMQ, AWS, and others due to its outstanding characteristics.


Ques. 17): What is the purpose of the Streams API?


Streams API is an API that allows an application to function as a stream processor, ingesting an input stream from one or more topics and providing an output stream to one or more output topics, as well as effectively changing the input streams to output streams.


Ques. 18): In Kafka, what do you mean by graceful shutdown?


Any broker shutdown or failure will be detected automatically by the Apache cluster. In this case, new leaders will be picked for partitions previously handled by that device. This can occur as a result of a server failure or even when the server is shut down for maintenance or configuration changes. Kafka provides a graceful approach for ending a server rather than killing it when it is shut down on purpose.

When a server is turned off, the following happens:

Kafka guarantees that all of its logs are synced onto a disc to avoid having to perform any log recovery when it is restarted. Purposeful restarts can be sped up since log recovery requires time.

Prior to shutting down, all partitions for which the server is the leader will be moved to the replicas. The leadership transfer will be faster as a result, and the period each partition is inaccessible will be decreased to a few milliseconds.


Ques. 19): In Kafka, what do the terms BufferExhaustedException and OutOfMemoryException mean?


A BufferExhaustedException is thrown when the producer can't assign memory to a record because the buffer is full. If the producer is in non-blocking mode and the pace of production over an extended period of time exceeds the rate at which data is transferred from the buffer, the allocated buffer will be emptied and an exception will be thrown.

An OutOfMemoryException may occur if the consumers send large messages or if the quantity of messages sent increases faster than the rate of downstream processing. As a result, the message queue becomes overburdened, using RAM.


Ques. 20): How will you change the retention time in Kafka at runtime?


A topic's retention time can be configured in Kafka. A topic's default retention time is seven days. While creating a new subject, we can set the retention time. When a topic is generated, the broker's property log.retention.hours are used to set the retention time. When configurations for a currently operating topic need to be modified, must be used.

The right command is determined on the Kafka version in use.

The command to use up to 0.8.2 is --alter.

Use --alter starting with version 0.9.0.



Tuesday, 23 November 2021

Top 20 AWS Database Interview Questions & Answers


Ques: 1). What are your thoughts on the Amazon Database?


Amazon Database is an Amazon Web Services offering that includes managed databases, managed services, and NoSQL. It also comes with a fully managed petabyte-scale data warehouse and in-memory caching as a service. There are four AWS database services to choose from, and the user can use one or all of them depending on their needs. DynamoDB, RDS, RedShift, and ElastiCache are the Amazon database services.

BlockChain Interview Question and Answers

Ques: 2). What are the features of Amazon Database?


Following are the important features of Amazon Database:

  • Easy to administer
  • Highly scalable
  • Durable and reliable
  • Faster performance
  • Highly available
  • More secure
  • Cost-effective


 Ques: 3). What is a key-value store, and how does it work?


A key-value store is a database service that makes it easier to store, update, and query items that are identified by their keys and values. These objects are made up of keys and values that make up the actual content that is saved.


Ques: 4).  What Is A Data Warehouse, And How Can Amazon Redshift Help With Storage?


A data warehouse can be conceived of as a repository for data acquired and stored from the company's systems and other sources. As a result, a data warehouse's design is three-tiered:

The tools that clean and collect data are found on the bottom rung.

We have tools in the intermediate layer that use Online Analytical Processing Server to alter the data.

We have various tools on the top layer that execute data analysis and data mining on the front end.

Setting up and maintaining a data warehouse costs a lot of money, especially as an organization's data grows and its data storage servers need to be upgraded on a regular basis. As a result, AWS RedShift was created, allowing businesses to store their data in Amazon's cloud-based warehouses.


AWS RedShift Interview Questions and Answers

Ques: 5). What Is The Difference Between A Leader Node And A Compute Node?


The queries from the client application are received in a leader node, where they are parsed and an execution plan is created. The stages for processing these queries are created, and the outcome is returned to the client application.

The steps allocated in the leader node are completed in a compute node, and the data is transferred. After that, the result is returned to the leader node before being delivered to the client application.


Ques: 6). What Is Amazon ElastiCache, and How Does It Work?


Amazon ElastiCache is an in-memory key-value store that can handle Redis and Memcached as key-value engines. It is a fully managed and zero administration service that Amazon has hardened. You may use Amazon ElastiCache to either create a new high-performance application or upgrade an existing one. ElastiCache has a wide range of applications in gaming, healthcare, and other fields.


Ques: 7). What Is Amazon ElastiCache's Purpose?


The caching of information that is utilised repeatedly could increase the performance of online applications. Using in-memory-caching, the data may be accessed very quickly. There is no need to manage a separate caching server with ElastiCache. An open source compatible in-memory data source with high throughput and low latency can be readily deployed or run.


Ques: 8). When would I prefer Provisioned IOPS over Standard RDS storage?


Provisioned IOPS deliver high IO rates but on the other hand it is expensive as well. Batch processing workloads do not require manual intervention they enable full utilization of systems, therefore a provisioned IOPS will be preferred for batch oriented workload.


Ques: 9). What Oracle features are available in AWS RDS?


Oracle is a well-known relational database that is available through Amazon RDS with enterprise version features. Almost every Oracle functionality may be used with the RDS platform.

If no version is specified when the database is created, it defaults to the most recent version available at the moment. In a Python SDK programme, here's an example of how to access the supported DB Engine versions using the AWS API.


Ques: 10). What are the differences between Amazon RDS, DynamoDB, and Redshift?


Amazon RDS is a relational database management service that handles patching, upgrading, and data backups for you without requiring your involvement. RDS is a database management service that exclusively handles structured data.

On the other hand, DynamoDB is a NoSQL database service, which works with unstructured data.

Redshift is a data warehouse product that is utilised in data analysis and is a completely different service.


Ques: 11). Can I use Amazon RDS to operate many database instances for free?


Yes. You can operate many Single-AZ Micro database instances, and they're all free! Any use of more than 750 instance hours across all Amazon RDS Single-AZ Micro DB instances, across all qualifying database engines and locations, will be paid at normal Amazon RDS charges. For example, if you run two Single-AZ Micro DB instances for 400 hours each in a month, you'll have 800 instance hours total, with 750 hours being free. The remaining 50 hours will be charged at the usual Amazon RDS rate.


Ques: 12). What is Oracle Licensing and how does it work?


Oracle licenses can be used in RDS in two ways:

Model with a License

The license for the software you'll use is held by Amazon in this model. Also, through its support programme, AWS provides support for both AWS and Oracle products. As a result, the user does not need to purchase a separate license. The user's licensing costs are included in the platform pricing.

Bring Your Own license

In this arrangement, the user imports her license into the RDS platform. It is the user's responsibility to keep the license, database instance class, and database edition all in sync. The user directly contacts the Oracle support channel for any need. In this model the supported editions are Enterprise Edition (EE), Standard Edition (SE), Standard Edition One (SE1) and Standard Edition Two (SE2).


Ques: 13). If I delete my DB Instance, what happens to my backups and DB Snapshots?


When you delete a database instance, you have the option of creating a final database snapshot, which you can use to restore your database. After the instance is removed, RDS keeps this user-made DB snapshot together with all other manually created DB snapshots. Automated backups are also deleted, leaving just manually created DB Snapshots.


Ques: 14).  How can I load data into Amazon Redshift from various data sources such as Amazon RDS, Amazon DynamoDB, and Amazon EC2?


You have two options for loading the data:

The COPY command can be used to load data into Amazon Redshift in parallel from Amazon EMR, Amazon DynamoDB, or any SSH-enabled server.

AWS Data Pipeline is a fault-tolerant, high-performance solution for loading data from a range of AWS data sources. To load your data into Amazon Redshift, you can utilise AWS Data Pipeline to specify the data source, required data transformations, and then run a pre-written import script.


Ques: 15). What is an RDS instance, and how does it work?


The Amazon Relational Database Service (Amazon RDS) is a web service that lets you easily construct a cloud-based relational database instance. Amazon RDS administers the database instance on your behalf, including backups, failover, and database software maintenance. Read Replicas, which are RDS instances that act as copies of the source master database for handling read-requests, can be launched for read-heavy applications. A source DB instance can have up to five (5) Read Replicas attached to it. The existing RDS Instances in the selected AWS region are listed on the Instances page. The information for an existing RDS Instance are displayed when you click on it.


Name - unique name/identifier for the RDS instance.

Engine - The version of the MySQL or Oracle engine of the RDS Instance.

RDS Subnet Group - The group of RDS Subnets for the VPC.

Availability Zone - The availability zone into which the RDS Instance will be created and launched.

Multi-AZ - Indicates that the RDS Instance will be used in a multiple availability zone configuration.

Instance class - If you selected a different instance type, the existing instance will be terminated and new RDS instance will be launched.

Storage - storage size in GBs for the instance that will be allocated for storing data.

Source instance - If the instance is a Read Replica, it will list the name of the source DB instance.

Status - The status of the RDS Instance (creating, modifying, available, rebooting, deleting). An RDS Instance will only be accessible when its status is 'available'.



Ques: 16). What is Amazon Aurora and how does it work?


Amazon Aurora is a form of cloud-based relational database that works with MySQL and PostgreSQL. It performs five times faster than MySQL and three times faster than PostgreSQL. The performance and availability of traditional databases are combined with the simplicity and cost-effectiveness of open-source databases in this hybrid database type. Because Amazon RDS manages this database completely, operations like hardware provisioning, database setup, patching, and backups are all automated.


Ques: 17). Which Amazon Web Services services will you use to collect and process e-commerce data in real time for analysis?


For real-time analysis, I'll utilise DynamoDB to collect and handle e-commerce data. DynamoDB is a fully managed NoSQL database service for unstructured data. It can even be used to extract e-commerce information from websites. RedShift may then be used to perform analysis on the retrieved e-commerce data. Elastic MapReduce can be utilised for analysis as well, but we won't use it here because real-time analysis isn't required.


Ques: 18). What happens if a user deletes a dB instance? What happens to the dB snapshots and backups?


The user is given the option of taking a last dB snapshot when a dB instance is removed. If you do so, your information from the snapshot will be restored. When the dB instance is removed, AWS RDS preserves all of the user-made dB snapshots together with all of the other manually created dB snapshots. Automated backups are erased at the same time, but manually produced dB snapshots are kept.


Ques: 19). What Is A Dynamodbmapper Class And How Does It Work?


The DynamoDB's entry point is the mapper class. It allows users to access the endpoint and input the DynamoDB. Users can use the DynamoDB mapper class to retrieve data stored in various databases, run queries, scan them against the tables, and perform CRUD activities on the data items.


Ques: 20). What is the RDS interface, and how does it work?


To use the RDS service, Amazon provides an RDS interface. An RDS interface is required to interact with the RDS service, such as reading data, uploading data, and running other programmes.

The GUI Console, Command Line Interface, and AWS API are the three main interfaces available.

A GUI Console is the most basic interface via which users can interact with the RDS Service.

The Command Line Interface (CLI) provides you with CLI access to the service, allowing you to run DB commands and interact with it.

An AWS API is an Application Programming Interface that allows two systems to exchange data.