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Top Apache NiFi Interview Questions and Answers Gaurav

Last updated on Feb 18 2022
Gaurav S Gaurav is a technology enthusiast working as a Sr. Research Analyst. He has expertise in domains like Big data, Artificial Intelligence and Cloud Computing

Top Apache NiFi Interview Questions and Answers

Table of Contents

1. Are you able to use the only installation of Ranger on the HDP, to be used with HDF?

Yes. you’ll use one Ranger installed on the HDP to manage HDF (separate installation) also. The Ranger that’s included with HDP won’t include the service definition for NiFi, so it might got to be installed manually.

2. Will NiFi have connectors following the RDBMS database?

Yes. Apache NiFi have connectors and we use rotate processors bundled in NiFi to act additionally than RDBMS in substitute ways.
For 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
• GenerateTableFetch permits you to not incrementally fetch the archives, however, and fetch neighbouring supply table partitions.

3. If you would like to execute a shell script, within the NiFi dataflow. the way to do that?

To execute a shell script within the NiFi processor, you’ll use the ExecuteProcess processor.

4. What’s the answer to avoid “Back-pressure deadlock”?

There are a couple of options like.
• Admin can temporarily increase the back-pressure threshold of the failed connection.
• Another useful approach to think about in such a case could also be to possess Reporting Tasks that might monitor the flow for giant queues.

5. Consider a scenario in which you consume a SOAP-based Webservice in HDF dataflow and WSDL. Which of the processor will help to consume this web service?

InvokeHTTP processor will help to consume this service.
With InvokeHTTP, you will be able to add dynamic properties, which can be sent within the request as headers. You will able to use dynamic properties to line values for the Content-Type and SOAPAction headers, just use the header names for the names of the dynamic properties. InvokeHTTP allows you to control the HTTP method, so you’ll set that to POST. The remaining step would be to urge the content of request.xml to be sent to the InvokeHTTP as a FlowFile. a method to try to to this is often to use a GetFile processor to fetch requeset.xml from some location on the filesystem and pass the success relationship of GetFile to InvokeHTTP.

6. How would you Distribute lookup data to be utilized in the Dataflow processor?

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

7. Will NiFi put in as a facilitate?

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

8. What’s the reportage Task?

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

9. Does the processor commit or rollback the session?

Yes, the processor is that the part through the session it’ll commit and rollback. If Processor rolls make sure 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.

10. 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 possess 170+ processors bundled behind the appliance by default, alongside the twitter processor. Moving promise considering, supplementary processors/extensions will tremendously be meant in all of freedom.

11. What’s the Template in NiFi?

Template may be a re-usable workflow. Which you’ll import and export within the same or different NiFi instances. It can save tons of your time instead of creating Flow, again and again, each time. A template is made as an XML file.

12. What’s a NiFi Custom Properties Registry?

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

13. What’s MiNiFi?

Answer. MiNiFi may be a subproject of Apache NiFi which is meant as a complementary data collection approach that supplements the core tenets of NiFi, that specialize in the gathering of knowledge at the source of its creation. MiNiFi is meant to run directly at the source, that’s why it’s special importance is given to the low footprint and low resource consumption. MiNiFi is out there in Java also as C++ agents which are ~50MB and three .2MB in size respectively.
14. What’s Apache NiFi used for?
• Reliable and secure transfer of knowledge between different systems.
• Delivery of knowledge from source to different destinations and platforms.
• Enrichment and preparation of knowledge.

• Conversion between formats.

• Extraction/Parsing.
• Routing decisions.

15. How can we decide between NiFi vs Flume cs Sqoop?

NiFi supports all use cases that Flume supports and even have Flume processor out of the box.
NiFi also supports some similar capabilities of Sqoop. as an example , GenerateTableFetch processor which does incremental fetch and parallel fetch against source table partitions.
Ultimately, what we would like to seem at is whether or not we are solving a selected or singular use case. IF so, then anybody of the tools will work. NiFi’s benefits will really shine once we consider multiple use cases being handled directly and important flow management features like interactive, real-time command and control with full data provenance.

16. Is There a programing language that Apache NiFi supports?

NiFi is implemented within the Java programing language and allows extensions (processors, controller services, and reporting tasks) to be implemented in Java. additionally , NiFi supports processors that execute scripts written in Groovy, Python, and a number of other other popular scripting languages.

17. How is Apache NiFi useful?

NiFi is useful in creating DataFlow. It means you’ll transfer data from one system to a different system also as process the info in between.

18. What’s a flow file?

FlowFiles are the guts of NiFi and its data flows. A FlowFile may be a data record, which consists of a pointer to its content and attributes which support the content. The content is that the pointer to the particular data which is being handled and therefore the attributes are key-value pairs that act as a metadata for the flow file. a number of the attributes of a flow file are filename, UUID, MIME Type etc.

19. what’s Reporting Task?

A Reporting Task may be a NiFi extension point that’s capable of reporting and analyzing NiFi’s internal metrics so as to supply the knowledge to external resources or report status information as bulletins that appear directly within the NiFi interface .

20. what’s NiFi Flowfile?

A FlowFile may be a message or event data or user data, which is pushed or created within the NiFi. A FlowFile has mainly two things attached thereto . 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).

21. What are the component of flow file?

A FlowFile is formed from two parts.

Content. The content may be a stream of bytes which contains a pointer to the particular data being processed within the data flow and is transported from source to destination. confine mind flow file itself doesn’t contain the info , rather it’s a pointer to the content data. the particular content are going to be within the Content Repository of NiFi.
Attributes. The attributes are key-value pairs that are related to the info and act because the metadata for the flowfile. These attributes are generally wont to store values which actually provides context to the info . a number of the samples of attributes are filename, UUID,
MIME Type, Flowfile creating time etc.

22. What’s the Bulleting and the way it helps in NiFi?

If you would like to understand if any problems occur during a data flow. you’ll sign up the logs for love or money interesting, it’s far more convenient to possess notifications crop up on the screen. If a Processor logs anything as a WARNING or ERROR, we’ll see a “Bulletin Indicator” show up within the top-right-hand corner of the Processor.
This indicator seems like a sticky note and can be shown for five minutes after the event occurs. Hovering over the bulletin provides information about what happened in order that the user doesn’t need to sift through log messages to seek out it. If during a cluster, the bulletin also will indicate which node within the cluster emitted the bulletin. we will also change the log level at which bulletins will occur within the Settings tab of the Configure dialog for a Processor.

23 . What’s 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 knowledge .
• Routing data from different source to destination.
• Event processing.
• End to finish provenance.
• Edge intelligence and bi-directional communication.

24 . What’s a processor?

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

25. How does NiFi Support Huge Volume of Payload during a Dataflow?

Huge volume of knowledge can transit from DataFlow. As data moves through NiFi, a pointer to the info is being passed around, mentioned as a flow file. The content of the flow file is merely accessed as required.

26 . NiFi and Kafka overlap in functionality?

This is quite common s. Apache NiFi and Kafka actually are very complementary solutions. A Kafka broker provides a really low latency especially once we have an outsized number of consumers pulling from an equivalent topic. Apache Kafka provides data pipelines and low latency, however, Kafka isn’t designed to unravel dataflow challenges i.e. data prioritization and enrichment etc. that’s what Apache NiFi is meant for, it helps in designing dataflow pipelines which may perform data prioritization and other transformations when moving data from one system to a different.
Furthermore, unlike NiFi, which handles messages with arbitrary sizes, Kafka prefers smaller messages, within the KB to MB range while NiFi is more flexible for varying sizes which may go up to GB per file or maybe more.
Apache NiFi is complementary to Apache Kafka by solving all the info flow problems for Kafka.

27 . What’s Relationship in NiFi Dataflow?

When a processor finishes with the processing of FlowFile. It may result in Failure or Success or the other relationship. And supported this relationship you’ll send data to the Downstream or next processor or mediate accordingly.

28 . While configuring a processor, what’s the language syntax or formulas used?

NiFi features 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.

29 . How does NiFi support huge volume of Payload during a Dataflow?

Huge volume of knowledge can transit from DataFlow. As data moves through NiFi, a pointer to the info is being passed around, mentioned as a FlowFile. The content of the FlowFile is merely accessed as required.

30 . If no prioritizers are set during a processor, what prioritization scheme is used?

The default prioritization scheme is claimed to be undefined, and it’s going to change from time to time. If no prioritizers are set, the processor will sort the info supported the FlowFile’s Content Claim. This way, it provides the foremost efficient reading of the info and therefore the highest throughput. we’ve discussed changing the default setting to First In First Out, but immediately it’s supported what gives the simplest performance.
These are a number of the foremost commonly used interview s regarding Apache NiFi. To read more about Apache NiFi you’ll check the category Apache NiFi and please do subscribe the newsletter for more related articles.

31 . What happens to data if NiFi goes down?

NiFi stores the info within the repository because 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’s streamed on to the content repository, when the processor finishes, it commits the session. This triggers the provenance repository to be updated to incorporate the events that occurred for that processor then the flowfile repository is updated to stay track of where within the flow the file is. Finally, the flow file are often moved to subsequent queue within the flow. This way, if NiFi goes down at any point, it’ll be ready to resume where it left off. This, however, glosses over one detail, which is that by default once we update the repositories, we write the into to repository but this is often often cached by the OS. just in case of any failure, this cached data could be lost if the OS also fails along side NiFi. If we actually want to avoid this caching, we will configure the repositories within the file NiFi properties to always sync to disk. This, however, are often a big hindrance to performance. If only NiFi does down this not be problematic in any thanks to data, as OS will still be liable for flushing that cached data to the disk.

32 . Does NiFi Works As A Master-slave Architecture?

No, from NiFi 1.0 there’s 0-master philosophy is taken into account . and every node within the NiFi cluster is that the same. NiFi cluster is managed by the Zookeeper. Apache ZooKeeper elects one node because the Cluster Coordinator, and failover is handled automatically by ZooKeeper. All cluster nodes report heartbeat and standing information to the Cluster Coordinator. The Cluster Coordinator is liable for disconnecting and connecting nodes. Additionally, every cluster has one Primary Node, also elected by ZooKeeper.

33. 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 meant to be performing on the principle of continuous streaming. Unless we select to only run a processor on an hourly or day to day for instance. But intentionally Apache NiFi isn’t a job-oriented thing. Once we start a processor, it runs continuously.

34. 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 entire point of separating metadata from the particular data. Essentially, one FlowFile represents an object or a message moving through NiFi. Each FlowFile contains a bit of content, which is that the actual bytes. you’ll then extract attributes from the content, and store them in memory. you’ll then operate against those attributes in memory, without touching your content. By doing so you’ll save tons of IO overhead, making the entire flow management process extremely efficient.

35 . what’s The Backpressure In NiFi System?

Sometimes what happens that Producer system is quicker than the buyer system. Hence, the messages which are consumed is slower. Hence, all the messages (FlowFiles) which aren’t being processed will remain within the connection buffer. However, you’ll limit the connection backpressure size either supported variety of FlowFiles or number of knowledge size. If it reaches to defined limit, a connection will refund pressure to producer processor not run. Hence, no more FlowFiles generated, until backpressure is reduced..

36 . Does NiFi Works As A Master-slave Architecture?

No, from NiFi 1.0 there’s 0-master philosophy is taken into account . and every node within the NiFi cluster is that the same. NiFi cluster is managed by the Zookeeper. Apache ZooKeeper elects one node because the Cluster Coordinator, and failover is handled automatically by ZooKeeper. All cluster nodes report heartbeat and standing information to the Cluster Coordinator. The Cluster Coordinator is liable for disconnecting and connecting nodes. Additionally, every cluster has one Primary Node, also elected by ZooKeeper.

37. What’s Apache NiFi?

NiFi is useful in creating DataFlow. It means you’ll transfer records from one gadget to a different device also as system the statistics in among.

38. What’s NiFi Flowfile?

A FlowFile may be 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).

39.What Is Relationship in NiFi Dataflow?

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

40. What’s Reporting Task?

A Reporting Task may be a NiFi extension factor that’s capable of reporting and reading NiFi’s inner metrics for you to supply the info to external resources or file reputation statistics as announcements that appear immediately inside the NiFi interface.

41. What’s A NiFi Processor?

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

42. Is There A programming language That Apache NiFi Supports?

NiFi is applied within the Java programming language and permits extensions (processors, controller offerings, and reporting tasks) to be administered in Java. additionally, NiFi supports processors that execute scripts written in Groovy, Python, and a number of other other popular scripting languages.

43. How does one Define NiFi Content Repository?

As we mentioned previously, contents aren’t saved within the FlowFile. they’re saved within the content repository and referenced via the FlowFile. this allows the contents of FlowFiles to be stored independently and successfully based on the underlying storage mechanism.

44. What’s the Backpressure In NiFi System?

Sometime what occurs that Producer system is quicker than purchaser gadget. Hence, the messages which could be ate up is slower. Hence, all of the messages (FlowFiles) which are not being processed will still be within the connection buffer. However, you’ll limit the connection 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.

45. What’s the Template in NiFi?

Template may be a re-usable workflow. Which you’ll import and export within an equivalent or distinct NiFi times. It can save lot of your time as against growing Flow again and again whenever. Template is made as an xml document.

46. What’s the Bulleting and the way It Helps In NiFi?

If you would like to understand if any problems occur during a dataflow. you’ll take a glance at inside the logs for whatever interesting, it is far more convenient to possess notifications crop up at the display. If a Processor logs whatever as a WARNING or ERROR, we’ll see a “Bulletin Indicator” display up within the pinnacle-right-hand corner of the Processor.
This indicator seems like a sticky notice and could be proven for five minutes after the occasion happens. Hovering over the bulletin offers records approximately what happened in order that the user does not need to sift through log messages to locate it. If during a cluster, the bulletin can also indicate which node within the cluster emitted the bulletin. We can also change the log stage at which announcements will occur inside the Settings tab of the Configure dialog for a Processor.

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

You can honestly add attributes on your FlowFiles at whenever, that’s the whole factor of isolating metadata from the important facts. Essentially, one FlowFile represents an object or a message moving thru NiFi. Each FlowFile contains a piece of content, that is the actual bytes. you’ll then extract attributes from the content material, and save them in memory. you’ll then operate against those attributes in reminiscence, without touching your content. By doing so you’ll shop many IO overheads, making the entire accompany the flow management process extremely efficient.

48. What Happens, If you’ve got Stored A Password during a Dataflow and make A Template Out Of It?

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

49. How Does NiFi Support Huge Volume Of Payload during 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 merely accessed as wished.

50. what’s a NiFi Custom Properties Registry?

You can use to load custom key, fee pair you’ll use custom homes registry, which may be configure as (in NiFi.Properties record)
NiFi.Variable.Registry.Properties=/conf/NiFi_registry
And you’ll place key cost pairs therein record and you’ll use that residences in you NiFi processor the usage of expression language e.G. $OS , once you have configured that belongings in registry document.

51. Does NiFi Works As A Master-slave Architecture?

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

52.What’s Apache NiFi?

Apache NiFi is an enterprise integration and dataflow automation tool that allows causing, receiving, routing, reworking, and modifying knowledge as needed and everybody this may be automatic and configurable. NiFi has the do one thing to associate united advocate systems and every second sort of supply and destinations have gone protocol, FTP, HDFS, arrangement, totally different databases, etc.

53.What’s MiNiFi?

MiNiFi might be a subproject of Apache NiFi that’s meant as a marginal knowledge amassing right of admission that supplements the core tenets of NiFi, focusing additionally as slightly to the p.s. of data at the availability of its set-in motion. MiNiFi is supposed to manage directly at the availability, 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 area unit ~50MB and three.2MB in size severally.

54.What’s the role of Apache NiFi within the large knowledge Ecosystem?

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

• Data acquisition and delivery.
• Transformations of data .
• Routing knowledge from interchange supply to destination.
• Event dispensing .
• End to mention no rootage.
• Edge acceptable judgment and bi-directional communication.

55.What square measures the foremost options of NiFi?

The main options of Apache NiFi are.

• Highly Configurable. Apache NiFi is deeply athletic in configurations and permits the us to seem at what nice of configuration we tend to twinge. as an example, variety of the possibilities are.
• Loss patient metal secured delivery
• Low latency vs High outturn
• Dynamic prioritization
• Flow is often changed at runtime
• Backpressure
• Designed for Extension. we are ready 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 might 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.

56.What’s Apache NiFi used for?

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

57.What’s a flow file?

FlowFiles area unit the middle of NiFi and its dataflows. A FlowFile might be a knowledge record, that consists of a pointer to its content and attributes that protruding to the content. The content is that the pointer to the actual knowledge that’s vertebrate handled and thus the attributes area unit key-value pairs that battle as information for the flow file. variety of the attributes of a flow file area unit file name, UUID, MIME Type, etc.

58.What’s the a part of the flow file?

A FlowFile is made happening of two parts.

1. Content. The content might be a stream of bytes that contains a pointer to the actual knowledge being processed within the dataflow and is transported from supply to destination. detain mind the flow file itself doesn’t contain the data, rather it’s a pointer to the content knowledge. the actual content can court case the Content Repository of NiFi.
2. Attributes. The attributes area unit key-value pairs that area unit connected following the data and suit due to the knowledge for the flow file. These attributes area unit usually won’t to grow values that really provides context to the data. variety of the samples of attributes area unit file name, UUID,
MIME Type, Flowfile making time, etc.

59.What’s a processor?

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

60.Do NiFi and author overlap in functionality?

This is totally common . Apache NiFi and author really totally substitute solutions. An author broker provides all low latency particularly once we’ve 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 supposed for, it helps in arising with knowledge flow pipelines which can manufacture consequences-dogfight knowledge prioritization and supplementary transformations behind perturbing data from one system to a special .
Furthermore, not like NiFi, that handles messages antecedently impulsive sizes, the author prefers smaller messages, within the pc memory unit to MB vary although NiFi is additional gymnastic for dynamic sizes which can 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.

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

NiFi features a conception referred to as exposure to atmosphere language that’s supported by taking into account associated with the subject of a per property basis, which suggests the developer of the processor will select whether or not a property supports outing language or not.

62.Is there a man-made language that Apache NiFi supports?

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

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

By default, the processors area unit already for eternity twist as Apache NiFi is supposed to be functioning regarding the principle of continuous streaming. Unless we tend to make a decision 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 put into the bureau a processor, it runs all the time.

64.However will we tend to ascertain 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 which will progressively fetch and parallel fetch closely supply table partitions.
Ultimately, what we’ve a bent to lack to publicize is whether or not or not we’ve a bent to face measure resolution a specific or singular use prosecution. IF consequently, later anybody of the tools can acquit yourself. NiFis foster can if truth be told shine within the rear, we’ve a bent to contemplate combination use cases bodily handled at taking into thought and really important flow dealing out options bearing in mind interactive, precise-time command and rule once full information rootage.

65.What happens to information if NiFi goes all along?

NiFi stores the data within the repository because it’s traversing through the system. There are unit 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 include the activities that occurred for that processor and afterward, the flow file repository is updated many |to avoid wasting”> to save lots of lots of track of wherever within the flow the file is. Finally, the flow files are often suffering from 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’s that by default following we’ve a bent to update the repositories, we’ve a bent to write down the into to repository however this is often {often|this can be} often cached by the OS. within the row of any failure, this cached information are often speculative if the OS fails on gone NiFi. If we’ve a bent to line sights on of reality nonentity to avoid this caching we are ready to found out the repositories within the knife properties file to perpetually suits disk. This, however, are often a serious 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.

66.If no prioritizer area unit set during a very processor, what prioritization plot is used?

The default prioritization theme is claimed to be undefined, and it’s getting to regulate from time to era. If no prioritizer area unit set, the processor can kind the data supported the FlowFiles Content Claim. This habit provides the foremost economical reading of the data and thus the very best output. we’ve got mentioned dynamical the default feels to initial In initial Out, however, immediately it’s based happening for what offers the foremost effective do its stuff.
These area unit variety of the foremost normally used interview queries vis–vis Apache NiFi. to travel surfing tons of terribly regarding Apache NiFi you’ll be ready to check the category Apache NiFi and entertain reach purchase the newssheet for tons of connected articles.

67.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.
68.Wherever will the content of FlowFile store?

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

69.Will NiFi put in as a facilitate?

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

70.What’s an association to NiFi dataflow?

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

71.What’s the reportage Task?

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

72.Will the processor commit or rollback the session?

Yes, the processor is that the part through the session it’ll commit and rollback. If Processor rolls make sure 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.

73.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’ve a bent to had 170+ processors bundled behind the appliance by default, along side the twitter processor. Moving promise considering, supplementary processors/extensions will tremendously be meant in all of freedom.

74.Will NiFi have any connectors following any RDBMS database?

Yes, you’ll be ready 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 tons of details on speaking exchange processors. https.//NiFi.apache.org/docs.html

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

NiFi includes a construct mentioned as discussion language that’s supported on the subject of a per-property basis, which suggests 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
76.Is there a programing language that Apache NiFi supports?

NiFi is enforced within the Java programing 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 a number of other other supplementary common scripting languages.

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

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

Any plans to involve on versioning to the NiFi docs concerning the subject of the Apache site? presently, i will be able to 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 plan to guarantee versioning to NIFI docs after we’ll .
I’m face to face an enormous aficionado of Apache NiFi, however, i’d want to know for several of the processors that area unit comprehensible within the Hortonworks knowledge Flow report of NiFi, a area unit they possible in Apache NiFi and may Apache NiFi still be actively developed as soon as additional appendage features?
HDF official pardon is, and may continually be, based upon Apache NiFi releases. For any longer NiFi options adscititious in HDF, Apache equivalents will fully be ancient.

78. What’s Apache NiFi?

Apache NiFi is enterprise integration and dataflow automation tool that permits sending, receiving, routing, transforming and modifying data as required and every one this will be automated and configurable. NiFi has the potential to attach multiple information systems and differing types of sources and destinations like HTTP, FTP, HDFS, filing system , Different databases etc.

79. What’s MiNiFi?

MiNiFi may be a subproject of Apache NiFi which is meant as a complementary data collection approach that supplements the core tenets of NiFi, that specialize in the gathering of knowledge at the source of its creation. MiNiFi is meant to run directly at the source, that’s why it’s special importance is given to the low footprint and low resource consumption. MiNiFi is out there in Java also as C++ agents which are ~50MB and three .2MB in size respectively.
80. what’s 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 knowledge .
Routing data from different source to destination.
Event processing.
End to finish provenance.
Edge intelligence and bi-directional communication.

81. What are the most features of NiFi?

The main features of Apache NiFi are.

Highly Configurable. Apache NiFi is very flexible in configurations and allows us to make a decision what quite configuration we would like . as an example , a number of the chances are.
Loss tolerant cs Guaranteed delivery
Low latency vs High throughput
Dynamic prioritization
Flow are often modified at runtime
Back pressure
Designed for extension. we will 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 may be a subproject of NiFi which reduces the footprint to approx. 40 MB only and is extremely useful once we got to run data pipelines in low resource environments.

82. What’s Apache NiFi used for?

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

83. What’s a flowfile?

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

84. What are the component of flowfile?

A FlowFile is formed from two parts.
Content. The content may be a stream of bytes which contains a pointer to the particular data being processed within the dataflow and is transported from source to destination. confine mind flowfile itself doesn’t contain the info , rather it’s a pointer to the content data. the particular content are going to be within the Content Repository of NiFi.
Attributes. The attributes are key-value pairs that are related to the info and act because the metadata for the flowfile. These attributes are generally wont to store values which actually provides context to the info . a number of the samples of attributes are filename, UUID,
MIME Type, Flowfile creating time etc.

85. What’s a processor?

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

86. Do NiFi and Kafka overlap in functionality?

This is quite common s. Apache NiFi and Kafka actually are very complementary solutions. A Kafka broker provides a really low latency especially once we have an outsized number of consumers pulling from an equivalent topic. Apache Kafka provides data pipelines and low latency, however Kafka isn’t designed to unravel dataflow challenges i.e., data prioritization and enrichment etc. that’s what Apache NiFi is meant for, it helps in designing dataflow pipelines which may perform data prioritization and other transformations when moving data from one system to a different.
Furthermore, unlike NiFi, which handles messages with arbitrary sizes, Kafka prefers smaller messages, within the KB to MB range while NiFi is more flexible for varying sizes which may go upto GB per file or maybe more.
Apache NiFi is complementary to Apache Kafka by solving all the dataflow problems for Kafka.

87. While configuring a processor, what’s the language syntax or formulas used?

NiFi features 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.

88. 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. additionally , NiFi supports processors that execute scripts written in Groovy, Python and a number of other other scripting languages.

89. 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 meant to be performing on the principle of continuous streaming. Unless we select to only run a processor on an hourly or day to day for instance. But intentionally Apache NiFi isn’t a job-oriented thing. Once we start a processor, it runs continuously.

90. How can we decide between NiFi vs Flume cs Sqoop?

NiFi supports all use cases that Flume supports and even have Flume processor out of the box.
NiFi also supports some similar capabilities of Sqoop. for instance, GenerateTableFetch processor which does incremental fetch and parallel fetch against source table partitions.
Ultimately, what we would like to seem at is whether or not we are solving a selected or singular use case. IF so, then anybody of the tools will work. NiFi’s benefits will really shine once we consider multiple use cases being handled directly and important flow management features like interactive, real-time command and control with full data provenance.

91. What happens to data if NiFi goes down?

NiFi stores the info within the repository because 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’s streamed on to the content repository, when the processor finishes, it commits the session. This triggers the provenance repository to be updated to incorporate the events that occurred for that processor then the flowfile repository is updated to stay track of where within the flow the file is. Finally, the flowfile are often moved to subsequent queue within the flow. This way, if NiFi goes down at any point, it’ll be ready to resume where it left off. This, however, glosses over one detail, which is that by default once we update the repositories, we write the into to repository but this is often often cached by the OS. just in case of any failure, this cached data could be lost if the OS also fails alongside NiFi. If we actually want to avoid this caching, we will configure the repositories within the NiFi.properties file to always sync to disk. This, however, are often a big hindrance to performance. If only NiFi does down this not be problematic in any thanks to data, as OS will still be liable for flushing that cached data to the disk.

92. If no prioritizes are set during a processor, what prioritization scheme is used?

The default prioritization scheme is claimed to be undefined, and it’s going to change from time to time. If no prioritizes are set, the processor will sort the info supported the FlowFile’s Content Claim. This way, it provides the foremost efficient reading of the info and therefore the highest throughput. we’ve discussed changing the default setting to First in First Out, but immediately it’s supported what gives the simplest performance.
These are a number of the foremost commonly used interview s regarding Apache NiFi. To read more about Apache NiFi you’ll check the category Apache NiFi and please do subscribe the newsletter for more related articles.

So, this brings us to the end of the Apache NiFi Interview Questions blog.
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If you wish to learn Apache NiFi and build a career in Big Data Testing domain, then check out our interactive, Apache NiFi Training, that comes with 24*7 support to guide you throughout your learning period.

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
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1 responses on "Top Apache NiFi Interview Questions and Answers Gaurav"

  1. Nice

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