Must-Know [LATEST] Apache NiFi Interview Questions and Answers

Last updated on 18th Nov 2021, Blog, Interview Questions

These Apache NiFi Interview Questions have been designed specially to get you acquainted with the nature of questions you may encounter during your interview for the subject of Apache NiFi. As per my experience, good interviewers hardly plan to ask any particular question during your interview, normally questions start with some basic concept of the subject, and later they continue based on further discussion and what you answer. we are going to cover the top 100 Apache NiFi Interview questions along with their detailed answers. We will be covering Apache NiFi scenario-based interview questions, Apache NiFi interview questions for freshers as well as Apache NiFi interview questions and answers for experienced.

1. What’s Apache NiFi?

Ans:

Apache NiFi is an enterprise integration and data flow automation tool that permits causing, receiving, routing, reworking, and modifying knowledge as required and everyone this will be automatic and configurable. NiFi has to 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.

2. What’s MiNiFi?

Ans:

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 it’s 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.

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

Ans:

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

• Data acquisition and delivery.>/li>
• 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.

4. What square measures the most options of NiFi?

Ans:

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 kind 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 facilitating and is tremendously helpful along within the thick of we, tend to add an additional marginal note to rule knowledge pipelines in low resource environments.

5. What’s Apache NiFi used for?

Ans:

Reliable and safe transfer of information 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.

6. What’s a flow file?

Ans:

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 stick 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.

7.What’s the part of the flow file?

Ans:

A FlowFile is formed happening of 2 parts:

• 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.
• 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, MIME Type, Flowfile making time, etc.

8. What’s a processor?

Ans:

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.

9.Do NiFi and author overlap in functionality?

Ans:

This is totally common question. 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.

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

Ans:

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.

11.What’s a NiFi Custom Properties Registry?

Ans:

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.

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

Ans:

Bz 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 an employment orienting matter. Once we tend to place into the bureau a processor, it runs all the time.

13. However will we tend to st es that Flume supports and includes a Flume processor out of the bin.

Ans:

NiFi as a consequence supports some of the 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.

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

Ans:

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

• The flow file repository.
• The content repository.
• 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 a NiFi. If we have a tendency to set sights on 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, is 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.

15. If no prioritizer square measure set in a very processor, what prioritization plot is used?

Ans:

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. These squares measure a number of the foremost normally used interview queries vis–vis Apache NiFi. To go surfing a lot of topics regarding Apache NiFi you’ll be able to check the class Apache NiFi and entertain each purchase the news sheet for a lot of connected articles.

16. Will a NiFi Flow file have unstructured information as expertly?

Ans:

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

17. Wherever will the content of FlowFile store?

Ans:

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

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

Ans:

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 to 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 dialogue for a Processor.

19. What’s an association to NiFi dataflow?

Ans:

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.

20. What’s the role of Apache NiFi in Big Data Ecosystem?

Ans:

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.

21.Will the processor commit or rollback the session?

Ans:

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 changing the FlowFile Repository and rootage Repository behind the relevant opinion.

22. Will NiFi member to external sources Like Twitter?

Ans:

Absolutely. NIFI includes an undoubtedly protractile framework, permitting any developers/users to hitch a knowledge supply instrumentation quite simply. Within the previous official pardon, NIFI 1.0, we have a tendency 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.

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

Ans:

Yes, you’ll be able to use rotated 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

24.What’s Relationship in NiFi Dataflow?

Ans:

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.

25.What’s the Template in NiFi?

Ans:

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.

26. During a Dataflow How does NiFi Support Huge Volume of Payload ?

Ans:

A 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.

27. If no priorities are set in a processor, what prioritization scheme is used?

Ans:

The default prioritization scheme is said to be undefined, and it may change from time to time. If no priorities 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.<;p>

28. What happens to data if NiFi goes down?

Ans:

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 this not be problematic in any way to data, as the OS will still be responsible for flushing that cached data to the disk.

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

Ans:

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

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

Ans:

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.

31. Does the processor commit or rollback the session?

Ans:

Yes, the processor is that the part through the session it’ll commit and roll back. 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 changing the FlowFile Repository and rootage Repository behind the relevant opinion.

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

Ans:

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.

33. Do NiFi and Kafka overlap in functionality?

Ans:

This is very common questions. 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.

34. What is a processor?

Ans:

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.

35. What are the components of flowfile?

Ans:

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 provide context to the data. Some of the examples of attributes are filename, UUID, MIME Type, Flowfile creating time etc.

36. What is a flowfile?

Ans:

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.

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

Ans:

The main roles Apache NiFi is suitable for in BigData Ecosystem are: Data acquisition and delivery. Transforamtions of data. Routing data from different source to destination. Event processing. End to end provenance. Edge intelligence and bi-directional communication.

38. What are the main features of NiFi?

Ans:

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 Guaranted delivery Low latency vs High throughput Dynamic prioritization Flow can be modified at runtime Back pressure Designed for extention: We can build our own processors and controllers etc. Secure SSL, SSH, HTTPS, encrypted content etc. Multi-tenant authorisation and internal authorisation/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.

39. Will NiFi put in as a facilitate?

Ans:

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

40. In this truck data example, do we need to write custom code in Kafka/Storm or is everything managed within NiFi components?

Ans:

In this example the only code that was written was the Storm topology to calculate the average speed over a window. The Storm topology made use of the provided KafkaSpout and KafkaBolt, and only required implementing two other bolts to parse the data and calculate the average. The data flow from source to Kafka was managed by MiNiFi and NiFi, and dataflow from Kafka to the dashboard was managed by NiFi.

41. Why Storm and not Spark for this example?

Ans:

Storm is the stream processing platform packaged with HDF, and this example was based on HDF for the overall architecture. A similar approach could be taken with Spark, or other stream processing platforms.

42. Isn’t PutKafka compatible (and recommended for kafka .9 and kafka .10) since with publishkafka ” there are cases where the publisher can get into an indefinite stuck state?”

Ans:

It is recommended to use the processor that is built against the Kafka client matching the broker being used. This means using PutKafka with an 0.8 broker, PublishKafka with a 0.9 broker, and PublishKafka_0_10 with a 0.10 broker.

43. Does NiFi have a backend to store data for a dashboard ?

Ans:

No, NiFi has internal repositories used to power the data flow, but these are not meant to build applications against. NiFi can be used to ingest data into many different tools that can be used to build dashboards. In this example, NiFi was ingesting data into Solr with a Banana dashboard.

44. Can NiFi connect to external sources Like Twitter?

Ans:

Absolutely. NIFI has a very extensible framework, allowing any developers/users to add a data source connector quite easily. In the previous release, NIFI 1.0, we had 170+ processors bundled with the application by default, including the twitter processor. Moving forward, new processors/extensions can definitely be expected in every release.

45. Does NiFi have any connectors with any RDBMS database?

Ans:

Yes, you can use different processors bundled in NiFi to interact with RDBMS in different ways. For example, “ExecuteSQL” allows you to issue a SQL SELECT statement to a configured JDBC connection to retrieve rows from a database; “QueryDatabaseTable” allows you to incrementally fetch from a DB table, and “GenerateTableFetch” allows you to not incrementally fetch the records, but also fetch against source table partitions. For more details regarding different processor

46. While configuring a processor, what is the language of syntax or formula used?

Ans:

NiFi has a concept called expression language which is supported on a per-property basis, meaning the developer of a processor can choose whether a property supports expression language. NiFi’s expression language is documented here: https://nifi.apache.org/docs/nifi-docs/html/expression-language-guide.html

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

Ans:

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 popular scripting languages.

48. Define Reporting Task?

Ans:

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 .

49.Any plans to add versioning to the NiFi docs on the Apache site? Currently, I can only find docs for 1.0.0, but .0.7.1 is the stable version, right?

Ans:

Great idea, we have filed a JIRA in Apache land to capture this thought. We definitely plan to add versioning to NIFI docs, as soon as we can.

50. What is Apache NiFi used for?

Ans:

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

51. What is MiNiFi?

Ans:

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.

52.what do you understand by NiFi Flowfile?

Ans:

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. It is content (Actual payload. Stream of bytes) and attributes. Attributes are key-value pairs attached to the content (You can say metadata for the content).

53. what do you understand by NiFi Flowfile?

Ans:

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. It is content (Actual payload. Stream of bytes) and attributes. Attributes are key-value pairs attached to the content (You can say metadata for the content).

54.What are the component of flow file?

Ans:

A FlowFile is formed from two parts.

Content : The content may be a stream of bytes that 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 is 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 of the metadata for the flow file. 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.

55. Will NiFi be put in as a facilitator?

Ans:

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

56. What is the reportage Task?

Ans:

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.

57. 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?

Ans:

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 be 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 do 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.

58. What is a Reporting Task?

Ans:

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.

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

Ans:

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.

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

Ans:

No, from NiFi 1.0 there is 0-master philosophy is considered. And each node in the NiFi cluster is the same. The 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.

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

Ans:

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.

62.How is Apache NiFi useful?

Ans:

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.

63. How Do You Define a NiFi Content Repository?

Ans:

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.

64. Why Should You Take Apache NiFi Training?

Ans:

The Average Salary for Apache NiFi Developers is $96,578 per year. – paysa.com

• Micron, Macquarie Telecom Group , Dovestech, Payoff, Flexilogix , Hashmap Inc. & many other MNC’s worldwide use Ansible across industries.
• 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.

65. Any plans to involve 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?

Ans:

Great idea, we’ve got 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.

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

Ans:

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 further ham it happening adjacent those attributes in memory, while not moving your content. By comporting yourself befittingly you’ll be able to save heaps of IO overhead, creating the collected flow running method positively economical.

67. Is there a programming language that Apache NiFi supports?

Ans:

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.

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

Ans:

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. His discussion language is documented here.

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

Ans:

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 get to be installed manually.

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

Ans:

The 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.

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

Ans:

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

• 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.

72. What’s an association to NiFi dataflow?

Ans:

Once a processor finishes taking into thought then 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.

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

Ans:

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.

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74. Will a NiFi Flow file have unstructured information as expertly?

Ans:

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

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

Ans:

NiFi as a consequence supports some of the 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.

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

Ans:

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, designing Apache NiFi isn’t an employment orienting matter. Once we tend to place a processor into the bureau, it runs all the time.

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

Ans:

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.

78. Will NiFi have connectors following the RDBMS database?

Ans:

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.

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

Ans:

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

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

Ans:

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.

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

Ans:

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

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

Ans:

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

83. Will NiFi be put in as a facilitated type ?

Ans:

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

84. What is the Template in NiFi?

Ans:

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.

85. How can we decide between NiFi vs Flume vs Sqoop?

Ans:

NiFi supports all use cases that Flume supports and also has 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.

86. What is Bulleting and How does it help in NiFi?

Ans:

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.

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

Ans:

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

88. What Is Relationship in NiFi Dataflow?

Ans:

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.

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

Ans:

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.

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

Ans:

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.

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

Ans:

The default prioritization scheme is said to be undefined, and it may change from time to time. If no priorities 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.

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

Ans:

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.

93. What is the Backpressure In NiFi System?

Ans:

Sometimes what happens is that the Producer system is faster than the consumer system. Hence, the messages which are consumed are 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 sizes. If it reaches a defined limit, a connection will give back pressure to the producer processor not run. Hence, no more FlowFiles generated, until backpressure is reduced.

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

Ans:

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.

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

Ans:

NiFi as a consequence supports some of the 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.

96. Will a NiFi Flow file have unstructured information as expertly?

Ans:

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

97. hat’s The Backpressure In NiFi System?

Ans:

Sometimes what happens is that the 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 to either a supported variety of FlowFiles or a number of knowledge sizes. If it reaches to a defined limit, a connection will refund pressure to the producer processor not run. Hence, no more FlowFiles are generated, until backpressure is reduced.

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

Ans:

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.

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

Ans:

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.

100. 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?

Ans:

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