Top Apache NiFi Interview Questions and Answers

Last updated on Feb 18 2022
Sunder Rangnathan

Table of Contents

What is Apache NiFi?

NiFi is helpful in creating DataFlow. It means you can transfer data from one system to another system as well as process the data in between.

What is a flow file?

FlowFiles are the heart of NiFi and its data flows. A Flow File is a data record, which consists of a pointer to its content and attributes which support the content. The content is the pointer to the actual data which is being handled and the attributes are key-value pairs that act as a metadata for the flow file. Some of the attributes of a flow file are filename, UUID, MIME Type etc.

What is Apache NiFi used for?

  • Reliable and secure transfer of data between different systems.
  • Delivery of data from source to different destinations and platforms.
  • Enrichment and preparation of data.
  • Conversion between formats.
  • Extraction/Parsing.
  • Routing decisions

What is MiNiFi?

MiNiFi is a subproject of Apache NiFi which is designed as a complementary data collection approach that supplements the core tenets of NiFi, focusing on the collection of data at the source of its creation. MiNiFi is designed to run directly at the source, that is why it is special importance is given to the low footprint and low resource consumption. MiNiFi is available in Java as well as C++ agents which are ~50MB and 3.2MB in size respectively.

If you want to execute a shell script, in the NiFi dataflow. How to do that?

To execute a shell script in the NiFi processor, you can use the ExecuteProcess processor.

What is the solution to avoid “Back-pressure deadlock”?

There are a few options like.

  • admin can temporarily increase the back-pressure threshold of the failed connection.
  • Another useful approach to consider in such a case may be to have Reporting Tasks that would monitor the flow for large queues.

If you want to consume a SOAP-based WebService in HDF dataflow and WSDL are provided to you. Which of the processor will help to consume this web service?

You can use the InvokeHTTP processor. With InvokeHTTP, you can add dynamic properties, which will be sent in the request as headers. You can use dynamic properties to set values for the Content-Type and SOAPAction headers, just use the header names for the names of the dynamic properties. InvokeHTTP lets you control the HTTP method, so you can set that to POST. The remaining step would be to get the content of request.xml to be sent to the InvokeHTTP as a FlowFile. One way to do this is to use a GetFile processor to fetch requeset.xml from some location on the filesystem and pass the success relationship of GetFile to InvokeHTTP.

Will NiFi put in as a facilitate?

Yes, it’s presently supported in the UNIX system and macOS lonesome.

What is the reportage Task?

A reportage Task may be a NiFi elaboration narrowing that is alert of reportage and analyzing NiFi’s internal metrics to gift the opinion to outside resources or report standing to warn as bulletins that seem directly within the NiFi interface.

Does the processor commit or rollback the session?

Yes, the processor is that the part through the session it will commit and rollback. If Processor rolls ensure the session, the FlowFile that were accessed throughout that session can each pension of 1 of being reverted to their previous states. If a Processor instead chooses to commit the session, the session is in command of change the FlowFile Repository and rootage Repository behind the relevant opinion.

What is Reporting Task?

A Reporting Task is a NiFi extension point that is capable of reporting and analyzing NiFi’s internal metrics in order to provide the information to external resources or report status information as bulletins that appear directly in the NiFi User Interface.

What is NiFi Flow file?

A FlowFile is a message or event data or user data, which is pushed or created in the NiFi. A FlowFile has mainly two things attached to it. Its content (Actual payload. Stream of bytes) and attributes. Attributes are key-value pairs attached to the content (You can say metadata for the content).

What is the component of flow file?

A Flow File is made up of two parts.

Content:  The content is a stream of bytes which contains a pointer to the actual data being processed in the data flow and is transported from source to destination. Keep in mind flow file itself does not contain the data, rather it is a pointer to the content data. The actual content will be in the Content Repository of NiFi.

Attributes:  The attributes are key-value pairs that are associated with the data and act as the metadata for the flowfile. These attributes are generally used to store values which actually provides context to the data. Some of the examples of attributes are filename, UUID,
MIME Type, Flowfile creating time etc.

What is a processor?

NiFi processors are the building block and most commonly used components in NiFi. Processors are the blocks which we drag and drop on the canvas and data flows are made up of multiple processors. A processor can be used for bringing data into the system like GetHTTPS, GetFile, ConsumeKafka etc. or can be used for performing some kind of data transformation or enrichment, for instance, SplitJSON, ConvertAvroToOrc, ReplaceText, ExecuteScript etc.

How does NiFi Support Huge Volume of Payload in A Dataflow?

Huge volume of data can transit from DataFlow. As data moves through NiFi, a pointer to the data is being passed around, referred to as a flow file. The content of the flow file is only accessed as needed.

Does NiFi Works As A Master-slave Architecture?

No, from NiFi 1.0 there is 0-master philosophy is considered. And each node in the NiFi cluster is the same. NiFi cluster is managed by the Zookeeper. Apache ZooKeeper elects a single node as the Cluster Coordinator, and failover is handled automatically by ZooKeeper. All cluster nodes report heartbeat and status information to the Cluster Coordinator. The Cluster Coordinator is responsible for disconnecting and connecting nodes. Additionally, every cluster has one Primary Node, also elected by ZooKeeper.

Can we schedule the flow to auto-run like one would with the coordinator?

By default, the processors are already continuously running as Apache NiFi is designed to be working on the principle of continuous streaming. Unless we select to only run a processor on an hourly or daily basis for example. But by design Apache NiFi is not a job-oriented thing. Once we start a processor, it runs continuously.

What Is Apache NiFi?

NiFi is helpful in creating DataFlow. It means you may transfer records from one gadget to another device as well as system the statistics in among.

What Is NiFi Flowfile?

A FlowFile is a message or occasion records or user records, that’s driven or created within the NiFi. A FlowFile has specifically matters connected with it. Its content (Actual payload. Stream of bytes) and attributes. Attributes are key fee pairs attached to the content material (You can say metadata for the content material).

What Is Relationship in NiFi Dataflow?

When a processor finishes with processing of FlowFile. It can bring about Failure or Success or any other courting. And based totally on this relationship you could send records to the Downstream or next processor or mediated as a consequence.

What Is Reporting Task?

A Reporting Task is a NiFi extension factor that is capable of reporting and reading NiFi’s inner metrics for you to offer the data to external resources or file reputation statistics as announcements that appear immediately inside the NiFi User Interface.

What is a NiFi Processor?

Processor is a major aspect inside the NiFi, that allows you to genuinely paintings at the FlowFile content material and allows in creating, sending, receiving, reworking routing, splitting, merging, and processing FlowFile.

Is There a Programming Language That Apache NiFi Supports?

NiFi is applied in the Java programming language and permits extensions (processors, controller offerings, and reporting tasks) to be carried out in Java. In addition, NiFi supports processors that execute scripts written in Groovy, Python, and several other popular scripting languages.

How Do You Define NiFi Content Repository?

As we referred to previously, contents aren’t saved within the FlowFile. They are saved within the content repository and referenced via the FlowFile. This permits the contents of FlowFiles to be stored independently and successfully primarily based on the underlying storage mechanism.

Does NiFi Works as a Master-slave Architecture?

No, from NiFi 1.0 there’s zero-master philosophy is considered. And each node within the NiFi cluster is the identical. NiFi cluster is managed by way of the Zookeeper. Apache ZooKeeper elects a single node because the Cluster Coordinator, and failover is handled robotically by means of ZooKeeper. All cluster nodes file heartbeat and status information to the Cluster Coordinator. The Cluster Coordinator is liable for disconnecting and connecting nodes. Additionally, every cluster has one Primary Node, additionally elected by ZooKeeper.

What’s Apache NiFi?

Apache NiFi is an enterprise integration and dataflow automation tool that permits causing, receiving, routing, reworking, and modifying knowledge as required and everyone this will be automatic and configurable. NiFi has the do one thing to associate united advocate systems and each second form of supply and destinations have gone protocol, FTP, HDFS, classification system, totally different databases, etc.

What’s MiNiFi?

MiNiFi could be a subproject of Apache NiFi that is meant as a marginal knowledge amassing right of admission that supplements the core tenets of NiFi, focusing in addition as a touch to the p.s. of information at the supply of its set-in motion. MiNiFi is meant to manage directly at the supply, that’s why its special importance is regulated to the low footprint and low resource consumption. MiNiFi is accessible in Java as deftly as C++ agents that square measure ~50MB and three.2MB in size severally.

What’s the role of Apache NiFi within the huge knowledge Ecosystem?

The main roles Apache NiFi is okay for in Big Data system are.

  • Data acquisition and delivery.
  • Transformations of information.
  • Routing knowledge from interchange supply to destination.
  • Event shelling out.
  • End to say no rootage.
  • Edge acceptable judgment and bi-directional communication.

What’s Apache NiFi used for?

  • Reliable and safe transfer of information on within the thick of periodical systems.
  • Delivery of information from supply to each second destination and platform.
  • Enrichment and preparation of data.
  • Conversion within the thick of formats.
  • Extraction/Parsing.
  • Routing choices.

What’s a flow file?

FlowFiles square measure the center of NiFi and its dataflows. A FlowFile could be a knowledge record, that consists of a pointer to its content and attributes that sticking out to the content. The content is that the pointer to the particular knowledge that is vertebrate handled and therefore the attributes square measure key-value pairs that battle as information for the flow file. a number of the attributes of a flow file square measure file name, UUID, MIME Type, etc.

What’s the part of the flow file?

A FlowFile is formed happening of 2 parts.

  1. Content: The content could be a stream of bytes that contains a pointer to the particular knowledge being processed within the dataflow and is transported from supply to destination. detain mind the flow file itself doesn’t contain the info, rather it’s a pointer to the content knowledge. the particular content can court case the Content Repository of NiFi.
  2. Attributes: The attributes square measure key-value pairs that square measure connected following the info and suit because of the information for the flow file. These attributes square measure usually won’t to grow values that truly provides context to the info. a number of the samples of attributes square measure file name, UUID,MIME Type, Flowfile making time, etc.

Do NiFi and author overlap in functionality?

This is totally common. Apache NiFi and author really totally substitute solutions. An author broker provides each one low latency particularly once we have an oversized range of shoppers actuation from the identical topics. Apache author provides knowledge pipelines and low latency, however, the author isn’t meant to resolve dataflow challenges i.e. knowledge prioritization and enrichment, etc. that’s what Apache NiFi is meant for, it helps in coming up with knowledge flow pipelines which may manufacture consequences-dogfight knowledge prioritization and supplementary transformations behind perturbing data from one system to a different.

Furthermore, not like NiFi, that handles messages antecedently impulsive sizes, the author prefers smaller messages, within the computer memory unit to MB vary although NiFi is additional gymnastic for dynamic sizes which may go up to GB per file or perhaps additional.

Apache NiFi is substituted to Apache {kafka|Kafka|Franz author|writer|author} by resolution of all the dataflow issues for Kafka.

Whereas configuring a processor, what’s the language syntax or formulas used?

NiFi features a conception known as exposure to atmosphere language that is supported by taking under consideration related to the topic of a per property basis, which means the developer of the processor will select whether or not a property supports outing language or not.

Is there an artificial language that Apache NiFi supports?

Apache NiFi is enforced in Java artificial language and permits for extensions to be enforced in Java. In adjoin NiFi supports processors that kill scripts written in Groovy, Jython, and a number of other auxiliary scripting languages.

What happens to information if NiFi goes all along?

NiFi stores the info within the repository because it is traversing through the system. There square measure three key repositories.

  1. The flow file repository.
  2. The content repository.
  3. The rootage repository.

As a processor writes information to a flow file, that’s streamed on to the content repository, bearing in mind the processor finishes, it commits the session. This triggers the rootage repository to be updated to incorporate the activities that occurred for that processor and later on, the flow file repository is updated to save lots of track of wherever within the flow the file is. Finally, the flow files are often affected by the likewise as-door-door queue within the flow. This exaggeration, if NiFi goes the length of at any narrowing, it’ll be adept to resume wherever it left off. This, however, glosses on the extremity of 1 detail, that is that by default following we have a tendency to update the repositories, we have a tendency to write the into to repository however this is {often|this can be} often cached by the OS. within the row of any failure, this cached information can be speculative if the OS fails on gone NiFi. If we have a tendency to set sights on of reality nonentity to avoid this caching, we are able to set up the repositories within the knife properties file to perpetually adjust to disk. This, however, are often a major hindrance to be in. If lonesome NiFi will the length of this not be problematic in any exaggeration to information, as OS can nonetheless be in command of flushing that cached information to the disk.

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.

This square measure a number of the foremost normally used interview queries vis–vis Apache NiFi. To go surfing a lot of terribly regarding Apache NiFi you’ll be able to check the class Apache NiFi and entertain reach purchase the newssheet for a lot of connected articles.

What’s an association to NiFi dataflow?

Once a processor finishes taking into thought than running of FlowFile. It will upshoot in Failure or Success or any more relationship. And supported this membership you’ll be able to send information to the Downstream or behind a processor or mediate consequently.

What’s the reportage Task?

Ans. A reportage Task may be a NiFi elaboration narrowing that’s alert of reportage and analyzing NiFi’s internal metrics so as to gift the opinion to outside resources or report standing to warn as bulletins that seem directly within the NiFi interface.

Will the processor commit or rollback the session?

Yes, the processor is that the part through the session it will commit and rollback. If Processor rolls ensure the session, the FlowFile that were accessed throughout that session can each pension of 1 of being reverted to their previous states. If a Processor instead chooses to commit the session, the session is in command of change the FlowFile Repository and rootage Repository behind the relevant opinion.

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.

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.

Secure: 

SSL, SSH, HTTPS, encrypted content etc.

Multi-tenant authorization and internal authorization/policy management

MiNiFi Subproject:  Apache MiNiFi is a subproject of NiFi which reduces the footprint to approx. 40 MB only and is very useful when we need to run data pipelines in low resource environments.

What is a flowfile?

FlowFiles are the heart of NiFi and its dataflows. A FlowFile is a data record, which consists of a pointer to its content and attributes which support the content. The content is the pointer to the actual data which is being handled and the attributes are key-value pairs that act as a metadata for the flowfile. Some of the attributes of a flowfile are filename, UUID, MIME Type etc.

 What are the component of flowfile?

A FlowFile is made up of two parts.

Content:  The content is a stream of bytes which contains a pointer to the actual data being processed in the dataflow and is transported from source to destination. Keep in mind flowfile itself does not contain the data, rather it is a pointer to the content data. The actual content will be in the Content Repository of NiFi.

Attributes:  The attributes are key-value pairs that are associated with the data and act as the metadata for the flowfile. These attributes are generally used to store values which actually provides context to the data. Some of the examples of attributes are filename, UUID,
MIME Type, Flowfile creating time etc.

While configuring a processor, what is the language syntax or formulas used?

NiFi has a concept called expression language which is supported on a per property basis, meaning the developer of the processor can choose whether a property supports expression language or not.

Is there a programming language that Apache NiFi supports?

Apache NiFi is implemented in Java programming language and allows for extensions to be implemented in Java. In addition, NiFi supports processors that execute scripts written in Groovy, Python and several other scripting languages.

What happens to data if NiFi goes down?

NiFi stores the data in the repository as it is traversing through the system. There are 3 key repositories.

The flowfile repository.

The content repository.

The provenance repository.

As a processor writes data to a flowfile, that is streamed directly to the content repository, when the processor finishes, it commits the session. This triggers the provenance repository to be updated to include the events that occurred for that processor and then the flowfile repository is updated to keep track of where in the flow the file is. Finally, the flowfile can be moved to the next queue in the flow. This way, if NiFi goes down at any point, it will be able to resume where it left off. This, however, glosses over one detail, which is that by default when we update the repositories, we write the into to repository but this is often cached by the OS. In case of any failure, this cached data might be lost if the OS also fails along with NiFi. If we really want to avoid this caching, we can configure the repositories in the NiFi.properties file to always sync to disk. This, however, can be a significant hindrance to performance. If only NiFi does down this not be problematic in any way to data, as OS will still be responsible for flushing that cached data to the disk.

If no prioritizes are set in a processor, what prioritization scheme is used?

The default prioritization scheme is said to be undefined, and it may change from time to time. If no prioritizes are set, the processor will sort the data based on the FlowFile’s Content Claim. This way, it provides the most efficient reading of the data and the highest throughput. We have discussed changing the default setting to First In First Out, but right now it is based on what gives the best performance.

These are some of the most commonly used interview s regarding Apache NiFi. To read more about Apache NiFi you can check the category Apache NiFi and please do subscribe to the newsletter for more related articles.

Can you use the single installation of Ranger on the HDP, to be used with HDF?

Answer. Yes. You can use a single Ranger installed on the HDP to manage HDF (separate installation) as well. The Ranger that is included with HDP will not include the service definition for NiFi, so it would need to be installed manually.

Will NiFi have connectors following the RDBMS database?

Yes. You will be able to use rotate processors bundled in NiFi to act additionally than RDBMS in substitute ways. as an example, ExecuteSQL permits you to the state of affairs a SQL choose statement to a designed JDBC association to burning rows from a database; QueryDatabaseTable permits you to incrementally fetch from a decibel table, and GenerateTableFetch permits you to not incrementally fetch the archives, however, and fetch neighboring supply table partitions.

If you want to execute a shell script, in the NiFi dataflow. How to do that?

To execute a shell script in the NiFi processor, you can use the ExecuteProcess processor.

What is the solution to avoid “Back-pressure deadlock”?

There are a few options like.

  • admin can temporarily increase the back-pressure threshold of the failed connection.
  • Another useful approach to consider in such a case may be to have Reporting Tasks that would monitor the flow for large queues.

If you want to consume a SOAP-based WebService in HDF dataflow and WSDL are provided to you. Which of the processor will help to consume this web service?

You can use the InvokeHTTP processor. With InvokeHTTP, you can add dynamic properties, which will be sent in the request as headers. You can use dynamic properties to set values for the Content-Type and SOAPAction headers, just use the header names for the names of the dynamic properties. InvokeHTTP lets you control the HTTP method, so you can set that to POST. The remaining step would be to get the content of request.xml to be sent to the InvokeHTTP as a FlowFile. One way to do this is to use a GetFile processor to fetch requeset.xml from some location on the filesystem and pass the success relationship of GetFile to InvokeHTTP.

How would you Distribute lookup data to be used in the Dataflow processor?

You should have used “PutDistributeMapCache”. to share common static configurations at various parts of a NiFi flow.

Will NiFi put in as a facilitate?

Yes, it’s presently supported in the UNIX system and macOS lonesome.

What is the reportage Task?

A reportage Task may be a NiFi elaboration narrowing that is alert of reportage and analyzing NiFi’s internal metrics to gift the opinion to outside resources or report standing to warn as bulletins that seem directly within the NiFi interface.

Does the processor commit or rollback the session?

Yes, the processor is that the part through the session it will commit and rollback. If Processor rolls ensure the session, the FlowFile that were accessed throughout that session can each pension of 1 of being reverted to their previous states. If a Processor instead chooses to commit the session, the session is in command of change the FlowFile Repository and rootage Repository behind the relevant opinion.

Will NiFi member to external sources Like Twitter?

Absolutely. NIFI includes an undoubtedly protractile framework, permitting any developers/users to hitch knowledge supply instrumentation quite simply. Within the previous official pardon, NIFI 1.0, we tend to have 170+ processors bundled behind the appliance by default, together with the twitter processor. Moving promise considering, supplementary processors/extensions will tremendously be meant in each one of freedom.

What is the Template in NiFi?

Template is a re-usable workflow. Which you can import and export in the same or different NiFi instances. It can save a lot of time rather than creating Flow, again and again, each time. A template is created as an XML file.

What is a NiFi Custom Properties Registry?

You can use to load custom key, value pair you can use custom properties registry, which can be configured as (in NiFi.properties file)
NiFi.variable.registry.properties=/conf/NiFi_registry
And you can put key-value pairs in that file and you can use those properties in you NiFi processor using expression language e.g. ${OS} if you have configured that property in a registry file.

How can we decide between NiFi vs Flume cs Sqoop?

NiFi supports all use cases that Flume supports and also have Flume processor out of the box.
NiFi also supports some similar capabilities of Sqoop. For example, GenerateTableFetch processor which does incremental fetch and parallel fetch against source table partitions.
Ultimately, what we want to look at is whether we are solving a specific or singular use case. IF so, then any one of the tools will work. NiFi’s benefits will really shine when we consider multiple use cases being handled at once and critical flow management features like interactive, real-time command and control with full data provenance.

Is There a Programming Language that Apache NiFi supports?

NiFi is implemented in the Java programming language and allows extensions (processors, controller services, and reporting tasks) to be implemented in Java. In addition, NiFi supports processors that execute scripts written in Groovy, Python, and several other popular scripting languages.

What is the Bulleting and How it helps in NiFi?

If you want to know if any problems occur in a data flow. You can check in the logs for anything interesting, it is much more convenient to have notifications pop up on the screen. If a Processor logs anything as a WARNING or ERROR, we will see a “Bulletin Indicator” show up in the top-right-hand corner of the Processor.
This indicator looks like a sticky note and will be shown for five minutes after the event occurs. Hovering over the bulletin provides information about what happened so that the user does not have to sift through log messages to find it. 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.

 What is the role of Apache NiFi in Big Data Ecosystem?

The main roles Apache NiFi is suitable for in Big Data 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.

NiFi and Kafka overlap in functionality?

This is very common s. Apache NiFi and Kafka actually are very complementary solutions. A Kafka broker provides a very low latency especially when we have a large number of consumers pulling from the same topic. Apache Kafka provides data pipelines and low latency, however, Kafka is not designed to solve dataflow challenges i.e. data prioritization and enrichment etc. That is what Apache NiFi is designed for, it helps in designing dataflow pipelines which can perform data prioritization and other transformations when moving data from one system to another.
Furthermore, unlike NiFi, which handles messages with arbitrary sizes, Kafka prefers smaller messages, in the KB to MB range while NiFi is more flexible for varying sizes which can go up to GB per file or even more.
Apache NiFi is complementary to Apache Kafka by solving all the data flow problems for Kafka.

 What Is Relationship in NiFi Dataflow?

When a processor finishes with the processing of FlowFile. It can result in Failure or Success or any other relationship. And based on this relationship you can send data to the Downstream or next processor or mediate accordingly.

 While configuring a processor, what is the language syntax or formulas used?

NiFi has a concept called expression language which is supported on a per property basis, meaning the developer of the processor can choose whether a property supports expression language or not.

How does NiFi support huge volume of Payload in a Dataflow?

Huge volume of data can transit from DataFlow. As data moves through NiFi, a pointer to the data is being passed around, referred to as a FlowFile. The content of the FlowFile is only accessed as needed.

 If no prioritizes are set in a processor, what prioritization scheme is used?

The default prioritization scheme is said to be undefined, and it may change from time to time. If no prioritizers are set, the processor will sort the data based on the FlowFile’s Content Claim. This way, it provides the most efficient reading of the data and the highest throughput. We have discussed changing the default setting to First In First Out, but right now it is based on what gives the best performance.
These are some of the most commonly used interview s regarding Apache NiFi. To read more about Apache NiFi you can check the category Apache NiFi and please do subscribe to the newsletter for more related articles.

 What happens to data if NiFi goes down?

NiFi stores the data in the repository as it is traversing through the system. There are 3 key repositories.

  1. The flow file repository.
  2. The content repository.
  3. The provenance repository.

As a processor writes data to a flowfile, that is streamed directly to the content repository, when the processor finishes, it commits the session. This triggers the provenance repository to be updated to include the events that occurred for that processor and then the flowfile repository is updated to keep track of where in the flow the file is. Finally, the flow file can be moved to the next queue in the flow. This way, if NiFi goes down at any point, it will be able to resume where it left off. This, however, glosses over one detail, which is that by default when we update the repositories, we write the into to repository but this is often cached by the OS. In case of any failure, this cached data might be lost if the OS also fails along with NiFi. If we really want to avoid this caching, we can configure the repositories in the file NiFi properties to always sync to disk. This, however, can be a significant hindrance to performance. If only NiFi does down this not be problematic in any way to data, as OS will still be responsible for flushing that cached data to the disk.

Do the Attributes get added to content (actual Data) when data is pulled by NiFi?

You can certainly add attributes to your FlowFiles at any time, that’s the whole point of separating metadata from the actual data. Essentially, one FlowFile represents an object or a message moving through NiFi. Each FlowFile contains a piece of content, which is the actual bytes. You can then extract attributes from the content, and store them in memory. You can then operate against those attributes in memory, without touching your content. By doing so you can save a lot of IO overhead, making the whole flow management process extremely efficient.

What is the Backpressure In NiFi System?

Sometimes what happens that Producer system is faster than the consumer system. Hence, the messages which are consumed is slower. Hence, all the messages (FlowFiles) which are not being processed will remain in the connection buffer. However, you can limit the connection backpressure size either based on a number of FlowFiles or number of data size. If it reaches to defined limit, a connection will give back pressure to producer processor not run. Hence, no more FlowFiles generated, until backpressure is reduced.

Does NiFi Works as A Master-slave Architecture?

No, from NiFi 1.0 there is 0-master philosophy is considered. And each node in the NiFi cluster is the same. NiFi cluster is managed by the Zookeeper. Apache ZooKeeper elects a single node as the Cluster Coordinator, and failover is handled automatically by ZooKeeper. All cluster nodes report heartbeat and status information to the Cluster Coordinator. The Cluster Coordinator is responsible for disconnecting and connecting nodes. Additionally, every cluster has one Primary Node, also elected by ZooKeeper.

 What Is the Backpressure in NiFi System?

Sometime what occurs that Producer system is quicker than purchaser gadget. Hence, the messages which might be fed on is slower. Hence, all of the messages (FlowFiles) which aren’t being processed will continue to be within the connection buffer. However, you can limit the relationship backpressure size either based totally on number of FlowFiles or range of records size. If it reaches to defined limit, connection will deliver lower back stress to producer processor not run. Hence, no greater FlowFiles generated, till backpressure is reduced.

What Is the Template in NiFi?

Template is a re-usable workflow. Which you may import and export within the same or distinct NiFi times. It can save lot of time as opposed to growing Flow again and again whenever. Template is created as an xml document.

What Is the Bulleting and How It Helps in NiFi?

If you need to realize if any problems occur in a dataflow. You can take a look at inside the logs for whatever interesting, it’s far much more convenient to have notifications pop up at the display. If a Processor logs whatever as a WARNING or ERROR, we will see a “Bulletin Indicator” display up within the pinnacle-right-hand corner of the Processor.

This indicator looks like a sticky notice and might be proven for 5 minutes after the occasion happens. Hovering over the bulletin offers records approximately what passed off so that the user does no longer have to sift through log messages to locate it. If in a cluster, the bulletin may also indicate which node in the cluster emitted the bulletin. We also can change the log stage at which announcements will occur inside the Settings tab of the Configure dialog for a Processor.

Do The Attributes Get Added to Content (actual Data) When Data Is Pulled By NiFi?

You can honestly add attributes on your FlowFiles at every time, that’s the entire factor of setting apart metadata from the real facts. Essentially, one FlowFile represents an object or a message moving thru NiFi. Each FlowFile contains a chunk of content, that’s the actual bytes. You can then extract attributes from the content material, and save them in memory. You can then operate against the ones attributes in reminiscence, without touching your content. By doing so you can shop lots of IO overhead, making the whole go with the flow management process extremely efficient.

What Happens, If You Have Stored a Password in A Dataflow and Create a Template Out of It?

Password is a sensitive belonging. Hence, whilst exporting the DataFlow as a template password could be dropped. As soon as you import the template in the same or exceptional NiFi machine.

 How Does NiFi Support Huge Volume of Payload In A Dataflow?

Huge volume of facts can transit from DataFlow. As data movements through NiFi, a pointer to the records is being passed around, called a FlowFile. The content material of the FlowFile is only accessed as wished.

What Is a NiFi Custom Properties Registry?

You can use to load custom key, fee pair you may use custom homes registry, which can be configure as (in NiFi.Properties record)

NiFi.Variable.Registry.Properties=/conf/NiFi_registry

And you could place key cost pairs in that record and you could use that residences in you NiFi processor the usage of expression language e.G. $OS , when you have configured that belongings in registry document.

What square measures the most options of NiFi?

The main options of Apache NiFi are.

  • Highly Configurable. Apache NiFi is deeply athletic in configurations and permits the United States to look at what nice of configuration we tend to twinge. as an example, a number of the probabilities are.
  • Loss patient metallic element secured delivery
  • Low latency vs High outturn
  • Dynamic prioritization
  • Flow are often changed at runtime
  • Backpressure
  • Designed for Extension. we are able to construct our own processors and controllers etc.
  • Secure
  • SSL, SSH, HTTPS, encrypted content etc.
  • Multi-tenant authorization and internal authorization/policy dispensation
  • MiNiFi Subproject. Apache MiNiFi could be a subproject of NiFi that reduces the footprint to approx. forty MB, while not facilitate and is tremendously helpful along within the thick of we, tend to additional marginal note to rule knowledge pipelines in low resource environments.

What’s a processor?

NiFi processors square measure the building block and therefore the most ordinarily used parts in NiFi. Processors square measure the blocks that we tend to drag and fall concerning the canvas and knowledge flows square measure created happening of compound processors. A processor is often used for transfer knowledge into the system considering GetHTTPS, GetFile, ConsumeKafka, etc. or are often used for interchange some nice of information transformation or enrichment, as an example, SplitJSON, ConvertAvroToOrc, ReplaceText, ExecuteScript, etc.

Will we tend to schedule the flow to automobile management once one would behind the coordinator?

By default, the processors square measure already for eternity twist as Apache NiFi is meant to be functioning regarding the principle of continuous streaming. Unless we tend to decide to unaided management a processor one thing bearing in mind AN hourly or day today as an example. However, designedly Apache NiFi isn’t employment orienting matter. Once we tend to place into the bureau a processor, it runs all the time.

However, will we tend to see that Flume supports and includes a Flume processor out of the bin.

NiFi as a consequence supports some same capabilities of Sqoop. as an example, GenerateTableFetch processor that will progressively fetch and parallel fetch closely supply table partitions.

Ultimately, what we have a tendency to lack to publicize is whether or not we have a tendency to square measure resolution a particular or singular use prosecution. IF consequently, later anybody of the tools can acquit yourself. NiFis foster can if truth be told shine within the back we have a tendency to contemplate combination use cases bodily handled at taking into thought and very important flow dealing out options bearing in mind interactive, precise-time command and rule once full information rootage.

Will NiFi Flow file have unstructured information as expertly?

Yes, FlowFile in NiFi will have each the Structured (e.g. XML, JSON files) as aptly as Unstructured (Image files) information.

Wherever will the content of FlowFile store?

FlowFile doesn’t accretion content itself. It stores the mention of the contents, that square measure keeps within the content repository.

Will NiFi put in as a facilitate?

Yes, it’s presently supported in the UNIX system and macOS lonesome.

What’s an association to NiFi dataflow?

Once a processor finishes taking into thought than running of FlowFile. It will upshoot in Failure or Success or any more relationship. And supported this membership you’ll be able to send information to the Downstream or behind a processor or mediate consequently.

What’s the reportage Task?

Ans. A reportage Task may be a NiFi elaboration narrowing that’s alert of reportage and analyzing NiFi’s internal metrics so as to gift the opinion to outside resources or report standing to warn as bulletins that seem directly within the NiFi interface.

Will the processor commit or rollback the session?

Yes, the processor is that the part through the session it will commit and rollback. If Processor rolls ensure the session, the FlowFile that were accessed throughout that session can each pension of 1 of being reverted to their previous states. If a Processor instead chooses to commit the session, the session is in command of change the FlowFile Repository and rootage Repository behind the relevant opinion.

Will NiFi member to external sources Like Twitter?

Absolutely. NIFI includes an undoubtedly protractile framework, permitting any developers/users to hitch knowledge supply instrumentation quite simply. Within the previous official pardon, NIFI 1.0, we have a tendency to had 170+ processors bundled behind the appliance by default, together with the twitter processor. Moving promise considering, supplementary processors/extensions will tremendously be meant in each one of freedom.

Will NiFi have any connectors following any RDBMS database?

Yes, you’ll be able to use rotate processors bundled in NiFi to act additionally than RDBMS in substitute ways. as an example, ExecuteSQL permits you to the state of affairs a SQL choose statement to a designed JDBC association to burning rows from a database; QueryDatabaseTable permits you to incrementally fetch from a decibel table and GenerateTableFetch permits you to not incrementally fetch the archives, however, and fetch neighboring supply table partitions. For a lot of details on speaking exchange processors. https.//NiFi.apache.org/docs.html

Whereas configuring a processor, what’s the language of syntax or formula used?

NiFi includes a construct referred to as discussion language that is supported on the topic of a per-property basis, which means the developer of a processor will choose whether or not a property supports drying language. is discussion language is documented here. https.//NiFi.apache.org/docs/NiFi-docs/html/exposure to feel-language-guide.html

Is there a programming language that Apache NiFi supports?

NiFi is enforced within the Java programming language and permits extensions (processors, controller facilities, and reportage tasks) to be enforced in Java. within the insert, NiFi supports processors that execute scripts written in Groovy, Jython, and several other supplementary common scripting languages.

Do the Attributes acquire adscititious to content (actual knowledge) following data is the force by NiFi

You can complete merger attributes to your FlowFiles at any time, which is the build-up improvement of separating data from the particular knowledge. primarily, one FlowFile represents AN outlook or a notice worrying through NiFi. every FlowFile contains a fraction of content, that is that the actual bytes. you’ll be able to furthermore extract attributes from the content, and adjoin them in memory. you’ll be able to furthermore ham it happening adjacent those attributes in memory, while not moving your content. By comport yourself befittingly you’ll be able to save heaps of IO overhead, creating the collected flow running method positively economical.

Any plans to involve on versioning to the NiFi docs relating to the topic of the Apache site? presently, I will on your own find docs for one.0.0, but .0.7.1 is that the stable comments, right?

Great idea, we’ve got filed a JIRA in Apache home to invade this thought. https.//issues.apache.org/jira/browse/NIFI-3005. we tend to positively arrange to guarantee versioning to NIFI docs after we will.

 I’m in person a huge aficionado of Apache NiFi, however, I might want to understand for several of the processors that square measure comprehensible within the Hortonworks knowledge Flow report of NiFi, a square measure they possible in Apache NiFi and can Apache NiFi still be actively developed as before long as additional appendage features?

HDF official pardon is, and can continually be, primarily based upon Apache NiFi releases. For any further NiFi options adscititious in HDF, Apache equivalents will fully be ancient.

What is Apache NiFi used for?

Reliable and secure transfer of data between different systems.

Delivery of data from source to different destinations and platforms.

Enrichment and preparation of data.

Conversion between formats.

Extraction/Parsing.

Routing decisions.

What is a processor?

NiFi processors are the building block and most commonly used components in NiFi. Processors are the blocks which we drag and drop on the canvas and dataflwos are made up of multiple processors. A processor can be used for bringing data into the system like GetHTTPS, GetFile, ConsumeKafka etc. or can be used for performing some kind of data transformation or enrichment, for instance, SplitJSON, ConvertAvroToOrc, ReplaceText, ExecuteScript etc.

Do NiFi and Kafka overlap in functionality?

This is very common s. Apache NiFi and Kafka actually are very complementary solutions. A Kafka broker provides a very low latency especially when we have a large number of consumers pulling from the same topic. Apache Kafka provides data pipelines and low latency, however Kafka is not designed to solve dataflow challenges i.e., data prioritization and enrichment etc. That is what Apache NiFi is designed for, it helps in designing dataflow pipelines which can perform data prioritization and other transformations when moving data from one system to another.

Furthermore, unlike NiFi, which handles messages with arbitrary sizes, Kafka prefers smaller messages, in the KB to MB range while NiFi is more flexible for varying sizes which can go upto GB per file or even more.

Apache NiFi is complementary to Apache Kafka by solving all the dataflow problems for Kafka.

Can we schedule the flow to auto run like one would with coordinator?

Bz default, the processors are already continuously running as Apache NiFi is designed to be working on the principle of continuous streaming. Unless we select to only run a processor on an hourly or daily basis for example. But by design Apache NiFi is not a job-oriented thing. Once we start a processor, it runs continuously.

How can we decide between NiFi vs Flume cs Sqoop?

NiFi supports all use cases that Flume supports and also have Flume processor out of the box.

NiFi also supports some similar capabilities of Sqoop. For example, GenerateTableFetch processor which does incremental fetch and parallel fetch against source table partitions.

Ultimately, what we want to look at is whether we are solving a specific or singular use case. IF so, then any one of the tools will work. NiFi’s benefits will really shine when we consider multiple use cases being handled at once and critical flow management features like interactive, real-time command and control with full data provenance.

 

So, this brings us to the end of the Apache NiFi Interview Questions blog.This Tecklearn ‘Top Apache NiFi Interview Questions and Answers’ helps you with commonly asked questions if you are looking out for a job in Apache NiFi or Big Data Domain. If you wish to learn Apache NiFi and build a career in Big Data domain, then check out our interactive, Apache NiFi Training, that comes with 24*7 support to guide you throughout your learning period.

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  • Apache NiFi is an open source software for automating and managing the flow of data between systems. It is a powerful and reliable system to process and distribute data. It provides a web-based User Interface for creating, monitoring, & controlling data flows.

What you will Learn in this Course?

Overview of Apache NiFi and its capabilities

  • Understanding the Apache NiFi
  • Apache NiFi most interesting features and capabilities

High Level Overview of Key Apache NiFi Features

  • Key features categories: Flow management, Ease of use, Security, Extensible architecture and Flexible scaling model

Advantages of Apache NiFi over other traditional ETL tools

  • Features of NiFi which make it different form traditional ETL tool and gives NiFi an edge over them

Apache NiFi as a Data Ingestion Tool

  • Introduction to Apache NiFi for data ingestion
  • Apache NiFi Processor : Data ingestion tools available for transferring , importing , loading and processing of data

Data Lake Concepts and Constructs (Big Data & Hadoop Environment)

  • Concept of data lake and its attributes
  • Support for colocation of data in various formats and overcoming the problem of data silos

Apache NiFi capabilities in Big Data and Hadoop Environment

  • Introduction to NiFi processors which sync with data lake and Hadoop ecosystem
  • An overview of the various components of the Hadoop ecosystem and data lake

Installation Requirements and Cluster Integration

  • Apache NiFi installation requirements and cluster integration
  • Successfully running Apache NiFi and addition of processor to NiFi
  • Working with attributes and Process of scaling up and down
  • Hands On

Apache NiFi Core Concepts

  • Apache NiFi fundamental concepts
  • Overview of FlowFile, Flow Controller ,FlowFile Processor, and their attributes
  • Functions in dataflow

Architecture of Apache NiFi

  • Architecture of Apache NiFi
  • Various components including FlowFile Repository, Content Repository, Provenance Repository and web-based user interface
  • Hands On

Performance Expectation and Characteristics of NiFi

  • How to utilize maximization of resources is particularly strong with respect to CPU and disk
  • Understand the best practices and configuration tips

Queuing and Buffering Data

  • Buffering of Data in Apache NiFi
  • Concept of queuing, recovery and latency
  • Working with controller services and directed graphs
  • Data transformation and routing
  • Processor connection, addition and configuration
  • Hands On

Database Connection with Apache NiFi

  • Apache NiFi Connection with database
  • Data Splitting, Transforming and Aggregation
  • Monitoring of NiFi and process of data egress
  • Reporting and Data lineage
  • Expression language and Administration of Apache NiFi
  • Hands On

Apache NiFi Configuration Best Practices

  • Apache NiFi configuration Best Practices
  • ZooKeeper access, properties, custom properties and encryption
  • Guidelines for developers
  • Security of Data in Hadoop and NiFi Kerberos interface
  • Hands On

Apache NiFi Project

  • Apache NiFi Installation
  • Configuration and Deployment of toolbar
  • Building a dataflow using NiFi
  • Creating, importing and exporting various templates to construct a dataflow
  • Deploying Real-time ingestion and Batch ingestion in NiFi
  • Hands On

Got a question for us? Please mention it in the comments section and we will get back to you.

 

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