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Showing posts with label message. Show all posts

January 04, 2022

Top 20 Apache Camel Interview Questions and Answers


                    The Apache Camel software is a message-oriented middleware system that is free and open source. The mediation takes place according to the guidelines that have been established. It's used by a lot of companies for data processing and analysis. Camel can be thought of as a routing engine at a high level. Camel allows us to establish routing rules for messages to be sent from a specific source to a specified destination.

                    Camel has built-in support for a variety of protocols, making it simple to interconnect diverse systems. Camel can easily integrate two separate apps that function with ftp and jms. Camel handles all of the protocol and datatype conversions for us internally.

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Ques. 1): What is Apache Camel, and how does it work?


There are a variety of oscillate systems in an organisation. Some of them could be legacy systems, while others could be new. These systems frequently interact in imitation of one another and require integration. Relationships or integration are more difficult than system implementations because message formats may differ. One method to achieve this is to agree on a code that bridges these gaps. However, there will be a decrease in mitigation integration as a result of this. If there is a fiddle contemplating in a system tomorrow, the press on may have to be tainted, which is not pleasant. Instead of this narrowing to mitigation integration which causes tight coupling, we can espouse a supplementary descent to mediate the differences along surrounded by the systems.

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Ques. 2): In Apache Camel, what are EIPs?


EIP (Enterprise Integration Patterns) is the abbreviation for Enterprise Integration Patterns. In the form of a pattern, these are design patterns for the usage of enterprise application integration and message-oriented middleware. Apache Camel makes use of a number of EIPs. Here are a few:

Splitter Pattern: Split the data on the basis of some token and then process it.

Content Based Router: The Content-Based Router inspects the content of a message and routes it to another channel based on the content of the message. Using such a router enables the message producer to send messages to a single channel and leave it to the Content-Based Router to inspect messages and route them to the proper destination. This alleviates the sending application from this task and avoids coupling the message producer to specific destination channels.

Message Filter: A Message Filter is a special form of a Content-Based Router. It examines the message content and passes the message to another channel if the message content matches certain criteria. Otherwise, it discards the message.

Recipient List: A Content-Based Router allows us to route a message to the correct system based on message content. This process is transparent to the original sender in the sense that the originator simply sends the message to a channel, where the router picks it up and takes care of everything.

Wire Tap: Wire Tap allows you to route messages to a separate location while they are being forwarded to the ultimate destination.

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Ques. 3): What are Apache Camel Components?


In Apache Camel, a component is a factory or a group of Endpoint instances. When using Spring or Guice, we can explicitly configure Component instances and connect them to a Camel Context in an IoC container. URIs can be used to discover components automatically.

Apache Camel comes with a lot of pre-built components. Some key Camel components from the core module are listed below.

  • Bean
  • Direct
  • File
  • Log
  • SEDA
  • Timer

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Ques. 4): What is an exchange in Apache camel?


The message that will be routed through the Camel route is already in the Exchange. It's the holder of the message. Message Exchange Patterns are used by Apache Camel (MEP). Any type of communication can be stored in an Apache camel exchange. It accepts a variety of file types, including XML, JSON, and others.

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Ques. 5): What is an ESB? Have you deployed a camel for any ESB?


Enterprise Service Bus (ESB) stands for Enterprise Service Bus. It is a tool that is used to help a severely pained application employing SOA concepts. When projects need integrating a number of Endpoints in front of Webservices, JMS, FTP, and other systems, an optimal unmodified ESB should be employed. JBoss Fuse ESB has been installed for Apache Camel Deployment.

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Ques. 6): What is a row in the Apache camel?


In the Exchange, the declaration to be routed via the Camel route is a gift. It is the possessor of the statement. Message Exchange Patterns are used by Apache Camel (MEP). Any nice of publication can be retained by Apache camel disputes. It supports a multitude of formats, including XML, JSON, and others.

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Ques. 7): What are the apache camel endpoints?


The Endpoint interface in Camel implements the Message Endpoint pattern. Endpoints are usually established by Components, and their URIs are used to refer to them in the DSL.

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Ques. 8): What is Apache JMeter?


JMeter is an Apache project that is used as a load balancing tool to evaluate the performance of a variety of facilities, including the most sophisticated web applications.

JMeter can also be used as a unit-testing tool for JDBC database connections, FTP, Web services, JMS, HTTP, and TCP connections, as well as OS native processes. JMeter can also be configured as a monitor, though this is more of a monitoring tool than a believer control tool, and it can be used for active scrutiny as well. Jmeter also fosters integration when compared to Selenium, allowing it to control automation scripts in addition to performing or load testing.


Ques. 9): What is the Idempotent Consumer pattern in Apache Camel?


We employ the Idempotent Consumer pattern in Apache Camel to filter out duplicate messages. Consider a case in which we must handle files without assistance while taking into account. Duplicates should be skipped if there are any. We may utilise Idempotent Consumer right within the component using Apache Camel, and it will skip files that have already been processed. The environment's idempotent=real choice will enable this feature. To do this, Apache Camel uses a statement-id, which is stored in the Idempotent Repository, to keep track of the consumed files. IdempotentRepository types are provided by Apache Camel.


Ques. 10): How does Apache Camel handle exceptions?


The try> catch> block, the OnException> block, or the errorHandler> block can all be used to handle exceptions.

Any uncaught Exception thrown during the routing and processing of a message is handled by the errorHandler. On the other hand, when certain Exception types are thrown, onException is used to manage them.


Ques. 11): What is CamelContext, and how does it work?


A single Camel routing rulebase is represented by the CamelContext. The CamelContext is comparable to the Spring ApplicationContext in that it is used in the same way. interface for the general public SuspendableService and RuntimeConfiguration are both extended by CamelContext. The context used to configure routes and policies to utilise during message exchanges between endpoints is represented by this interface.


Ques. 12): What is the best way to restart CamelContext?


To govern the camel lifecycle, the camel context provides many methods such as start, stop, suspend, and resume. A service can be restarted using one of two techniques. Stopping the context and then starting it is one option. It's known as a cold restart, and it clears the internal state, cache, and other data to render all endpoints useless. Suspend and resume operations are another option. It saves all of your endpoints so you can use them again after you restart.


Ques. 13): What is an URI?


In camel, a URI is a naming system for referring to an endpoint. An URI informs Camel about the component being used, the context path, and the options that have been applied to it. The URI is made up of three parts:

  • Scheme
  • Context path
  • Options

Example of a file URI working as a consumer –


Here the scheme points to file, the context path is “src/data” and the options are “fileName and fileExist” are options that can be used with file component or file endpoint.


Ques. 14): In Apache Camel, what is the Idempotent Consumer pattern?


We employ the Idempotent Consumer pattern in Apache Camel to filter out duplicate messages. Consider the case where we only need to process messages once. Duplicates should be skipped if there are any. We can utilise Idempotent Consumer directly within Apache Camel to bypass messages that have already been processed. The idempotent=true option is used to enable this feature. To do this, Apache Camel uses a message id, which is stored in the Idempotent Repository, to keep track of the consumed messages. IdempotentRepository is a kind of IdempotentRepository provided by Apache Camel.


Ques. 15): How to connect with Azure from Apache Camel?


Yes. We can connect with azure services using Apache camel connector components.

Maven Dependency:





  <!-- use the same version as your Camel core version -->


Code Example:



Ques. 16): What is a Message in Apache Camel ?


Message implements the Message pattern and represents an inbound or outbound message as part of an Exchange.It contains the data being transferred using Routes- It consists of the following fields-

  • Unique Identifier
  • Headers
  • Body
  • Fault Flag


Ques. 17): What is the relationship between Apache Camel and ActiveMQ?


Apache Camel is embedded in the ActiveMQ broker using the ActiveMQ component. It allows you a lot of versatility when it comes to extending the message broker. Additionally, the ActiveMQ component eliminates the serialisation and network costs associated with connecting to ActiveMQ remotely.

This component is built on the JMS component and allows users to send and consume messages over the JMS Queue.


Ques. 18): How do I disable JMX in Apache Camel?


The JMX instrumentation agent is enabled in Camel by default. To disable the JMX instrumentation agent, set the following property in the Java VM system property,


Another way of disabling is by adding the JMX agent element inside the camel context element in the Spring configuration,

<camelContext id=”camel” xmlns=””>

<jmxAgent id=”agent” disabled=”true”/>




Ques. 19): What Jars Do I Need?

Answer :

Camel is designed to be small lightweight and extremely modular so that you only pay for what you use. The core of camel, camel-core.jar is small and has minimal dependencies.

On Java 6 camel-core.jar only depends on

commons-management.jar (for Camel 2.8 or older)

commons-logging.jar (for Camel 2.6 or older)

slf4j-api.jar (from Camel 2.7 onwards)

On Java 5 camel-core.jar depends also on activation.jar and a JAXB2 implementation which typically involves jaxb-api.jar, jaxb-impl.jar and a StAX API which may be stax-api.jar and *woodstox.jar


Ques. 20): What Is The Difference Between A Producer And A Consumer Endpoint?


A camel route is similar to a data channel in that it transports data. At either end of the channel, there are two endpoints: producer and consumer.

The route's beginning point is called a consumer endpoint. Writing a camel consumer endpoint is the first step in defining a camel route.

A producer endpoint appears at the end of the route (but not always). The data that is passed through the route is consumed by it.


January 03, 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.

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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.

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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.

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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.

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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.

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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.



November 17, 2021

Top 20 Apache ActiveMQ Interview Questions & Answers


Ques: 1). What exactly is ActiveMQ?


Apache Message-oriented middleware (MOM) is a type of software that transmits messages between applications, and ActiveMQ is one of them. ActiveMQ facilitates loose coupling of elements in an IT system using standards-based, asynchronous communication, which is frequently basic to business messaging and distributed applications. Messages are translated from sender to receiver using ActiveMQ. Instead of requiring both the client and the server to be online at the same time in order to interact, it can connect numerous clients and servers and allow messages to be queued.

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Ques: 2). In Apache ActiveMQ, what are clusters?


Load balancing of messages on a queue between consumers is supported by ActiveMQ in a stable and high-performance manner. This scenario is known as the competing consumers pattern in corporate integration. The principle is illustrated in the diagram below: Interview questions for Activemq

The burden is distributed in an extremely fluid manner. In high-load periods, more consumers might be provisioned and joined to the queue without changing any queue setup, as the new consumer would behave like any other competing consumer. Better availability than load-balanced systems. To determine whether real-servers are offline, load balancers often use a monitoring system. A failed consumer will not compete for messages if there are competing consumers, so messages will not be given to it even if it is not monitored.

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Ques: 3). What Is The Difference Between ActiveMQ And AmQP?


The Advanced Message Queue Protocol is a wire-level protocol for client-to-messaging-broker communication that serves as a specification for how messaging clients and brokers will interact.

AMQP is a message protocol rather than a messaging system like ActiveMQ.

Open wire protocols, such as OpenWire, a fast binary format, are supported by AMQP.

Stomp is a text-based protocol that is simple to implement.

MQTT is a little binary format designed for restricted devices over a shaky network.

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Ques: 4). What distinguishes ActiveMQ from other messaging systems?


  • It is a Java messaging service implementation, therefore it contains all of Java's features.
  • Extremely persistent
  • It has a high level of security and authentication.
  • Various brokers can form a cluster and collaborate with one another.
  • ActiveMQ offers a number of client APIs in a range of languages.

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Ques: 5). What are the most important advantages of ActiveMQ?


  • Allows users to combine many languages with various operating systems.
  • Allows for location transparency.
  • Communication that is both reliable and effective
  • It's simple to scale up and offers asynchronous communication.
  • Reduced coupling

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Ques: 6). What are ActiveMQ's biggest drawbacks?


It's a complicated mechanism that only allows one thread per connection.

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Ques: 7), In ActiveMQ, what is a topic?


Virtual Topics are a hybrid of Topics and Queues, with listeners consuming messages from the queues as messages to the topic.

ActiveMQ assists in replicating and duplicating every message from the topic to the actual consumers queues.

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Ques: 8). What is the difference between Activemq and Fuse Message Broker?


Multiple Protocol Messaging is a Java-based message broker that supports industry-standard protocols and allows users to choose from a wide range of client languages, including JavaScript, C, C++, and Python.

Fuse Message Broker is a distributor of FuseSource's Apache ActiveMQ, which it develops and updates as part of the Apache ActiveMQ community.

Bug fixes are more likely to come from the Fuse Broker release than from an official Apache ActiveMQ release.

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Ques: 9). What exactly is KahaDB?


KahaDB is a file-based persistence database that runs on the same machine as the message broker. It has been designed to be persistent in a short amount of time. Since ActiveMQ 5.4, it has been the default storage mechanism. Compared to its predecessor, the AMQ Message Store, KahaDB consumes fewer file descriptors and recovers faster.


Ques: 10). What exactly is LevelDB?


LevelDB is a somewhat faster index than KahaDB, with slightly better performance figures. The LevelDB store will allow replication in forthcoming ActiveMQ releases.


Ques: 11). What's the difference between RabbitMQ and ActiveMQ?


ActiveMQ is a Java-scripted open-source message broker that is built on the Java Message Service client. The RabbitMQ protocol is based on the Advanced Message Queuing protocol.


Ques: 12). What are the benefits of using a combination of topics and queues instead of traditional topics?


There will be no lost communications even if a customer is offline. All messages are copied to the queues that have been registered by ActiveMQ.

A dead letter queue will be set up if a customer is unable to process a message. Without affecting the other consumers, the consumer can be resolved and the message forwarded to his own dedicated queue.

To implement a load balancing mechanism we can register multiple instances of a consumer on a queue.


Ques: 13). If the ActiveMQ server is unavailable, what should I do?


This begins with ActiveMQ's storage mechanism. Non-persistent messages are stored in memory under typical conditions, while persistent messages are stored in files, with their maximum limitations set in the configuration file's node. When the number of non-persistent messages reaches a specific threshold and memory becomes scarce, ActiveMQ will write the non-persistent messages in memory to a temporary file to free up space Despite the fact that they are all saved in files, the distinction between persistent messages and non-persistent temporary files is that persistent messages will be restored from the file after restart, whereas non-persistent temporary files would be removed immediately.


Ques: 14). What happens if the file size exceeds the configuration's maximum limit?


Set a 2GB persistent file limit and mass-produce persistent messages until the file exceeds its limit. The producer is currently prohibited, but the consumer can connect and consume the message as usual. The producer can continue to transmit messages after a portion of the message has been eaten and the file has been erased to make room, and the service will automatically revert to normal.

Set a 2GB limit on temporary files, mass-produce non-persistent messages, and write temporary files. When the maximum limit is reached, the producer is blocked, and consumers can still connect but not consume messages, or consumers who were previously sluggish consumers suddenly consume Stop. The complete system is linked, but it is unable to give services, causing it to hang.


Ques: 15). What is message-oriented middleware, and how does it work?


Message-oriented middleware (MOM) is a software or hardware framework that allows distributed systems to send and receive messages. MOM simplifies the development of applications that span different operating systems and network protocols by allowing application modules to be distributed across heterogeneous platforms. The middleware establishes a distributed communications layer that hides the intricacies of the multiple operating systems and network interfaces from the application developer.


Ques: 16). What is the benefit of Activemq over other options such as databases?


Activemq is a messaging system that allows two distributed processes to communicate successfully. It can keep messages in a database to communicate between processes, but you'd have to erase them as soon as they were received. For each message, this means a row insert and remove. When you try to scale that up to hundreds of messages per second, databases start to break down.

Message-oriented middleware, such as ActiveMQ, is designed to handle these scenarios. They assume that messages will be erased promptly in a healthy system and can make optimizations to prevent the overhead. It can also push messages to consumers rather than requiring them to poll for fresh messages via SQL queries. This minimises the amount of time it takes for new messages to be processed into the system.


Ques: 17). What are some of the platforms supported by ActiveMQ?


Some of the common platforms supported by ActiveMQ include:

Any java platform that has an update of 5.0 or more.

J2EE 1.4 is another platform

JMS 1.1

JCA 1.5 resource adaptor


Ques: 18). Make a distinction between ActiveMQ and Mule.


ActiveMQ is a messaging service with a lot of options for both the broker and the client. Mule, on the other side, is an ESB that may provide executive functionality to merely the broker by exchanging messages between various software components.

Mule's architecture is such that it is designed to provide a programming configuration that is feasible for integrating applications between a database and an operating system. Mule, on the other hand, does not support any form of native messaging system, hence it is typically used in conjunction with ActiveMQ. the user is required to introduce different and unique frameworks to define various boundaries for connectivity.


Ques: 19). What is the process for dealing with an application server using JMS connections?


The server session is created with the help of an application server, which then stores them in a pool. An association buyer uses the server's session to place messages in JMS sessions. The JMS session is created by a server session. The messaging audience is created by an application produced by application software engineers.


Ques: 20). What distinguishes ActiveMQ from the spread toolkit?


Spread Toolkit is a C++ library for informing, with only rudimentary support for JMS. It does not support robust informing, exchanges, XA, or JMS 1.1 in its entirety. It's also depending on a locally installed version of Spread inspiration. Apache ActiveMQ, on the other hand, is the JMS provider used in Apache Geronimo. It is J2EE 1.4 certified in Geronimo and is a completely pure version of the Java programming language. ActiveMQ supports real-time and persistent messaging, exchanges, XA, J2EE 1.4, JMS 1.1, JCA 1.5, and a slew of other features like Message Groups and Clustering.