Apache NiFi is a robust, easy-to-use, and dependable solution for data processing and distribution across heterogeneous platforms. Simply put, NiFi was created to automate the exchange of data across systems. It is based on the NSA-developed Niagara Files (NiFi) technologies, which were handed to the Apache Software Foundation after eight years.
Without any need for coding, Apache NiFi enables users to swiftly and precisely define data flows. Due to the breadth of its capabilities, numerous firms worldwide are embracing Apache NiFi, necessitating a continual demand for Apache NiFi certified personnel. The Apache NiFi certification is critical to achieving your ideal profession since it verifies that you have acquired all of the essential abilities to execute critical tasks in the real world. However, after completing certification training, you must pass the interview round in order to obtain the job you seek.
As a result, we've compiled a list of the top 30 Apache NiFi Interview Questions, which might assist you in acing the interview.
Apache NiFi is a free and open-source application that automates and manages data flow across systems. It is a secure and dependable data processing and distribution system that incorporates a web-based user interface for the purpose of creating, monitoring, and controlling data flows. It features a highly customizable and changeable data flow method that allows for real-time data modification.
The Processor is a critical component of the NiFi since it is responsible for actually executing the FlowFile data and assists in producing, transmitting, receiving, converting, routing, dividing, integrating, and analyzing FlowFile.
A FlowFile is a file that contains signal, event, or user data that is pushed or generated in the NiFi. A FlowFile is mostly composed of two components. Its data and attributes. Attributes are key-value pairs that are associated with a piece of content or data.
MiNiFi is a project of NiFi that is intended to enhance the fundamental concepts of NiFi by emphasizing the gathering of data at its generation source. MiNiFi is meant to operate at the source, which is why it places a premium on the minimal area and resource utilization.
Yes, with NiFi, a FlowFile may include both organized (XML, JSON files) and complex (graphics) data.
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A Processor Node is a shell all around the Processor that manages the processor's state. The Processor Node is responsible for maintaining the
Positioning of processors in the graph.
Processor configuration characteristics.
Scheduling the processor's states.
A Reporting Task is a NiFi expansion endpoint that is responsible for reporting and analysing NiFi's inner statistics in order to transmit the data to other sources or to display status data straight in the NiFi UI.
Yes, the processor is the module that may submit and reverse data via the session. When a Processor starts rolling back a session, all FlowFiles retrieved during the session are restored to their prior states. If, on the other hand, the Processor decides to submit the session, it will update the FlowFile repositories with the necessary information.
This implies that any changes made to the FlowFileRepository will be first logged and checked for consistency. Remain in the logs to avoid data loss, both before and during data processing, as well as checkpoints on a frequent basis to facilitate reversal.
No, a Reporting Task has no access to the contents of any specific FlowFile. Rather than that, a Reporting Task gets accessibility to all Provenance Events, alerts, and metrics associated with graph components, like Bits of data, read or written.
It assists in determining when this FlowFile must be terminated and destroyed after a certain period of time. Assume you've set FlowFileExpiration to 1 hour. As soon as the FlowFile is detected in the NiFi platform, the countdown begins. Furthermore, once FlowFile reaches the connection, it will verify the age of the FlowFile; if it is older than 1 hour, the FlowFile will be ignored and destroyed.
Occasionally, the producer system outperforms the consumer system. As a result, the communications are slower. Hence, all unprocessed messages (FlowFiles) will stay in the network buffer. However, you may restrict the magnitude of the network backpressure depending on the number of FlowFiles or the quantity of the data. If it exceeds the set limit, the link will return pressure to the producing processor, causing it to stop running. As a result, no more FlowFiles are created until the backpressure is removed.
No, the settings of the processor cannot be altered or modified while it is operating. You must first halt it and then allow for all FlowFile processing to complete. Then and only then may you modify the processor's settings.
RouteOnAttribute permits the system to make congestion control within the flow, allowing certain FlowFiles to be treated differently than others.
A template is a workflow that may be reused, which you may import and export across many NiFi instances. It can save a lot of time compared to generating Flow repeatedly. The template is produced in the form of an XML file.
NiFi maintains a Data provenance library that contains all information about the FlowFile. As data continues to flow and is converted, redirected, divided, consolidated, and sent to various endpoints, all of this metadata is recorded in NiFi's Provenance Repository. Users can conduct a search for the processing of every single FlowFile.
This FlowFile property indicates the date and time the FlowFile was added or generated in the NiFi system. Even if a FlowFile is copied, combined, or divided, a child FlowFile may be generated. However, the lineageStartDate property will provide the timestamp for the ancestor FlowFile.
Numerous algorithms are available, including ExtractText, EvaluateXQuery which can help you get data from the FlowFile attribute. Furthermore, you may design your own customized microprocessor to meet the same criteria if no off-the-shelf processor is provided.
When a template is produced via DataFlow and if it has an associated ControllerService, a new instance of the control system service will be generated throughout the import process.
A password is a very sensitive piece of information. As a result, when publishing the DataFlow as templates, the password is removed. Once you export the template into another NiFi system, whether the same or a different one, you must enter the password once more.
While you can review the archives for anything noteworthy, having notifications come up on the board is far handier. If a Process records something as a WARNING, a "Bulletin Indicator" will appear in the Processor. This indication, which resembles a sticky note, will be displayed for five minutes following the occurrence of the event. If the bulletin is part of a cluster, it will additionally specify which network device released it. Furthermore, we may alter the log frequency at which bulletins are generated
A process group can assist you in developing a sub-data stream that users can include in your primary data flow. The destination address and the input port are used to transmit and receive information from the process group, respectively.
The flow controller is the project's brain that allocates threads to run modifications and maintains the scheduling for when modules obtain resources to execute. The Flow Controller functions as the engine, determining when a thread is assigned to a specific processor.
DataFlow can handle massive amounts of data. As data flows via NiFi, referred to as a FlowFile, is handed around, the FlowFile's information is only accessible when necessary.
The FlowFile Library is where NiFi stores information about a particular FlowFile that is currently online in the stream.
The Content Repository stores the exact bytes of a FlowFile's information.
Assume you're using a processor, such as PublishJMS, to release the information to the target list. The destination queue, on the other hand, is full, and your FlowFile will be sent to the failed relationship. And when you retry the unsuccessful FlowFile, the incoming backpressure linkage becomes full, which might result in a backpressure stalemate.
There are several alternatives, including
The administrator can temporarily boost the failed connection's backpressure level.
Another option to explore in this scenario is to have Reporting Tasks monitoring the flow for big queues.
This is accomplished by implementing an effective permanent write-ahead logging and information repository.
Yes, you may handle HDF with a single Ranger deployed on the HDP. Nevertheless, the Ranger that comes with HDP doesn't include NiFi service definition, and so must be installed manually.
No, starting with NiFi 1.0, the 0-master principle is taken into account. Furthermore, each unit in the NiFi network is identical. The Zookeeper service manages the NiFi cluster. Apache ZooKeeper appoints a single point as the Cluster Administrator, and ZooKeeper handles redundancy seamlessly.
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