Showing posts with label kafka. Show all posts
Showing posts with label kafka. 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.

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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.



Wednesday, 17 November 2021

Top 20 Apache NiFi Interview Questions & Answers


Ques: 1). Is there a functional overlap between NiFi and Kafka?


This is a pretty typical question, and the situation is actually extremely complementary. When you have a large number of customers drawing from the same topic, a Kafka broker gives very low latency. However, Kafka isn't built to tackle dataflow problems - imagine data prioritisation and enrichment — Kafka isn't built for that. Furthermore, unlike NIFI, which can handle messages of any size, Kafka prefers smaller messages in the KB to MB range, whereas NiFi can accept files up to GB per file or more. NiFi is an add-on to Kafka that solves all of Kafka's dataflow issues.


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Ques: 2). What is Apache NiFi, and how does it work?


Apache NiFi is a dataflow automation and enterprise integration solution that allows you to send, receive, route, alter, and modify data as needed, all while being automated and configurable. NiFi can connect many information systems as well as several types of sources and destinations such as HTTP, FTP, HDFS, File System, and various databases.

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Ques: 3). Is NiFi a viable alternative to ETL and batch processing?


For certain use situations, NiFi can likely replace ETL, and it can also be utilised for batch processing. However, the type of processing/transformation required by the use case should be considered. Flow Files are used in NiFi to define the events, objects, and data that pass through the flow. While NiFi allows you to perform any transformation per Flow File, you shouldn't use it to combine Flow Files together based on a common column or perform certain sorts of windowing aggregations. Cloudera advises utilising extra solutions in this situation.

The ideal choice in a streaming use scenario is to have the records transmitted to one or more Kafka topics utilising NiFi's record processors. Based on our acquisition of Eventador, you can then have Flink execute any of the processing you want on this data (joining streams or doing windowing operations) using Continuous SQL.

NiFi would be treated as an ELT rather than an ETL in a batch use scenario (E = extract, T = transform, L = load). NiFi would collect the various datasets, do the necessary transformations (schema validation, format transformation, data cleansing, and so on) on each dataset, and then transmit the datasets to a Hive-powered data warehouse. Once the data is sent there, NiFi could trigger a Hive query to perform the joint operation.

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Ques: 4). Is Nifi a Master-Server Architecture?


No, the 0-master philosophy has been considered since NiFi 1.0. In addition, each node in the NiFi cluster is identical. The Zookeeper is in charge of the NiFi cluster. ZooKeeper chooses a single node to serve as the Cluster Coordinator, and ZooKeeper handles failover for you. The Cluster Coordinator receives heartbeat and status information from all cluster nodes. The Cluster Coordinator is in charge of detaching and reconnecting nodes in the cluster. Every cluster also has one Primary Node, which is chosen by ZooKeeper.

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Ques: 5). What is the role of Apache NiFi in Big Data Ecosystem?


The main roles Apache NiFi is suitable for in BigData Ecosystem are:

Data acquisition and delivery.

Transformations of data.

Routing data from different source to destination.

Event processing.

End to end provenance.

Edge intelligence and bi-directional communication.

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Ques: 6). What are the component of flowfile?


There are two sections to a FlowFile:

Content: The content is a stream of bytes that transports from source to destination and contains a pointer to the actual data being processed in the dataflow. Keep in mind that the flowfile is merely a link to the content data, not the data itself. The actual content will be stored in NiFi's Content Repository.

Attributes: The attributes are key-value pairs that are associated with the data and serve as the flowfile's metadata. These characteristics are typically used to store values that give meaning to the data. Filename, UUID, and other properties are examples. MIME Type, Flowfile creating time etc.


Ques: 7). What exactly is the distinction between MiNiFi and NiFi?


Agents called MiNiFi are used to collect data from sensors and devices in remote areas. The purpose is to assist with data collection's "initial mile" and to obtain data as close to its source as possible.

These devices can include servers, workstations, and laptops, as well as sensors, self-driving cars, factory machinery, and other devices where you want to collect specialised data using MiNiFi's NiFi features. Before transferring data to a destination, the ability to filter, select, and triage it.

The objective of MiNiFi is to manage this entire process at scale with Edge Flow Manager so the Operations or IT teams can deploy different flow definitions and collect any data as the business requires. Here are some details to consider:

To move data around or collect data from well-known external systems like databases, object stores, and so on, NiFi is designed to be centrally situated, usually in a data centre or in the cloud. In a hybrid cloud architecture, NiFi should be viewed as a gateway for moving data back and forth between diverse environments.

MiNiFi connects to a host, does some processing and logic, and only distributes the data you care about to external data distribution platforms. Of course, such systems can be NiFi, but they can also be MQTT brokers, cloud provider services, and so on. MiNiFi also enables use scenarios where network capacity is constrained and data volume transferred over the network must be reduced.

MiNiFi is available in two flavours: C++ and Java. The MiNiFi C++ option has a modest footprint (a few MBs of memory, a small CPU), but a limited number of processors. The MiNiFi Java option is a single-node lightweight version of NiFi that lacks the user interface and clustering capabilities. It does, however, necessitate the presence of Java on the host.


Ques: 8). Will we be able to arrange the flow to automotive management after the coordinator is in place?


As Apache NiFi is designed to work on the idea of continuous streaming, the processors are already set for eternity twist by default. Unless we opt to handle a processor without assistance, for example, on an hourly or daily basis today. Apache NiFi, on the other hand, isn't supposed to be a job-oriented matter. When we put a processor in the bureau, it operates all of the time.


Ques: 9). What are the main features of NiFi?


The main features of Apache NiFi are.

Highly Configurable: Apache NiFi is highly flexible in configurations and allows us to decide what kind of configuration we want. For example, some of the possibilities are.

Loss tolerant cs Guaranteed delivery

Low latency vs High throughput

Dynamic prioritization

Flow can be modified at runtime

Back pressure

Designed for extension:We can build our own processors and controllers etc.


SSL, SSH, HTTPS, encrypted content etc.

Multi-tenant authorization and internal authorization/policy management


Ques: 10). Is there a NiFi connector for any RDBMS database?


Yes, different processors included in NiFi can be used to communicate with RDBMS in various ways. For example, "ExecuteSQL" lets you issue a SQL SELECT statement to a configured JDBC connection to retrieve rows from a database; "QueryDatabaseTable" lets you incrementally fetch from a DB table; and "GenerateTableFetch" lets you not only incrementally fetch the records, but also against source table partitions.


Ques: 11). What is the best way to expose REST API for real-time data collection at scale?


Our customer utilises NiFi to expose a REST API allowing data to be sent to a destination from external sources. HTTP is the most widely used protocol.

If you want to ingest data, you'll utilise the ListenHTTP processor in NIFi, which you may configure to listen to a certain port for HTTP requests and deliver any data to.

Look at the HandleHTTPRequest and HandleHTTPResponse processors if you wish to implement a web service with NiFi. You will receive an HTTP request from an external client if you use the two processors together. You'll be able to respond to the customer with a customised answer/result based on the data in the request. For example, you can use NiFi to connect to remote systems via HTTP, such as an FTP server. The two processors would be used, and the request would be made over HTTP. When NIFi receives a query, it runs a query on the FTP server to retrieve the file, which is then returned to the client.

NiFi can handle all of these one-of-a-kind needs with ease. In this scenario, NiFi would scale horizontally to meet the needs, and a load balancer would be placed in front of the NiFi instances to distribute the load throughout the cluster's NiFi nodes.


Ques: 12). When NiFi pulls data, do the attributes get added to the content (real data)?


You may absolutely add attributes to your FlowFiles at any moment; after all, the purpose of separating metadata from actual data is to allow you to do so. A FlowFile is a representation of an object or a message travelling via NiFi. Each FlowFile has a piece of content, which are the bytes themselves. The properties can then be extracted from the material and stored in memory. You can then use those properties in memory to perform operations without having to touch your content. You can save a lot of IO overhead this way, making the entire flow management procedure much more efficient.


Ques: 13). Is it possible for NiFi to link to external sources such as Twitter?


Absolutely. NIFI's architecture is extremely flexible, allowing any developer or user to quickly add a data source connector. We had 170+ processors packaged with the application by default in the previous edition, NIFI 1.0, including the Twitter processor. Every release will very certainly include new processors/extensions in the future.


Ques: 14). What's the difference between NiFi and Flume cs Sqoop?


NiFi supports all of Flume's use cases and includes the Flume processor out of the box.

Sqoop's features are also supported by NiFi. GenerateTableFetch, for example, is a processor that performs incremental and concurrent fetches against source table partitions.

At the end of the day, we want to know if we're solving a specific or unique use case. If that's the case, any of the tools will suffice. When we consider several use cases being handled at once, as well as essential flow management features like interactive, real-time command and control with full data provenance, NiFi's benefits will really shine.


Ques: 15).What happens to data if NiFi goes down?


As data moves through the system, NiFi stores it in the repository. There are three important repositories:

The flowfile repository.

The content repository.

The provenance reposiroty.

When a processor finishes writing data to a flowfile that is streamed directly to the content repository, it commits the session. This updates the provenance repository to include the events that occurred for that processor, and it also updates the flowfile repository to maintain track of where the file is in the flow. Finally, the flowfile can be moved to the flow's next queue.

NiFi will be able to restart where it left off if it goes down at any point. This, however, overlooks one detail: when we update the repositories, we write the into the repository by default, but the OS frequently caches this. If the OS dies together with NiFi in the event of a failure, the cached data may be lost. If we absolutely want to eliminate caching, we can set the file's repositories to always sync to disc. This, on the other hand, can be a severe impediment to performance. If NiFi goes down, it will have no effect on data because the OS will still be responsible for flushing the cached data to the disc.


Ques: 16). What Is The Nifi System's Backpressure?


Occasionally, the producer system outperforms the consumer system. As a result, the messages consumed are slower. As a result, all unprocessed communications (FlowFiles) will be stored in the connection buffer. However, you can set a restriction on the magnitude of the connection backpressure based on the number of FlowFiles or the quantity of the data. If it exceeds a predetermined limit, the link will send back pressure to the producing processor, causing it to stop working. As a result, until the backpressure is removed, no new FlowFiles will be generated.


Ques: 17). What Is Bulleting In Nifi And How Does It Help?


If you want to know if a dataflow has any issues. You can look through the logs for anything intriguing, but having notifications appear on the screen is far more convenient. A "Bulletin Indicator" will appear in the top-right-hand corner of the Processor if it logs anything as a WARNING or ERROR.

This sign, which resembles a sticky note, will appear for five minutes after the incident has occurred. By hovering over the bulletin, the user can get information about what happened without having to search through log messages. If in a cluster, the bulletin will also indicate which node in the cluster emitted the bulletin. We can also change the log level at which bulletins will occur in the Settings tab of the Configure dialog for a Processor.


Ques: 18). When Nifi pulls data, do the attributes get added to the content (real data)?


You may absolutely add attributes to your FlowFiles at any moment; after all, the purpose of separating metadata from actual data is to allow you to do so. A FlowFile is a representation of an object or a message travelling via NiFi. Each FlowFile has a piece of content, which are the bytes themselves. The properties can then be extracted from the material and stored in memory. You can then use those properties in memory to perform operations without having to touch your content. You can save a lot of IO overhead this way, making the entire flow management procedure much more efficient.


Ques: 19). What prioritisation scheme is utilised if no prioritizers are set in a processor?


The default priority strategy is described as "undefined," and it is subject to change. If no prioritizers are specified, the processor will order the data using the Content Claim of the FlowFile. It delivers the most efficient data reading and the highest throughput this way. We've debated changing the default setting to First In First Out, but for now, we're going with what works best.


Ques: 20). If no prioritizer square measure set in a very processor, what prioritization plot is used?


The default prioritization theme is claimed to be undefined, and it’s going to regulate from time to era. If no prioritizer square measure set, the processor can kind the info supported the FlowFiles Content Claim. This habit provides the foremost economical reading of the info and therefore the highest output. we’ve got mentioned dynamical the default feels to initial In initial Out, however, straight away it’s primarily based happening for what offers the most effective do its stuff.

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