Nifi generate flowfile example

By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service. The dark mode beta is finally here. Change your preferences any time. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information.

I have a flowfile with 3 attributes a, b, c. I tried different combinations, but not able to generate hash.

nifi generate flowfile example

Some permutations work, but then I don't know if it is generating correct has. I created following template and ran it. NiFi comes with a processor called HashAttribute. The usage of which is like, you can add dynamic properties where the name of the new property would be the name of the FlowFile and the value is a regex, you can provide? Learn more.

Asked 1 year, 6 months ago. Active 1 year, 6 months ago. Viewed 1k times. Update I created following template and ran it. Rakesh Prasad. Rakesh Sebastian yatra ft dalmata - sutra Rakesh Prasad 3 3 silver badges 14 14 bronze badges. Active Oldest Votes. Sivaprasanna Sethuraman Sivaprasanna Sethuraman 3, 3 3 gold badges 23 23 silver badges 46 46 bronze badges.

Please look at my update. That is because it hashes the key and value together. So, even when you have same values, the keys i. FlowFile attribute names are different and that is why you're getting different values.

Take a look at this block: github. Andy Andy Sign up or log in Sign up using Google. Sign up using Facebook. Sign up using Email and Password. Post as a guest Name. Email Required, but never shown. The Overflow Blog. Podcast Programming tutorials can be a real drag. Featured on Meta. Community and Moderator guidelines for escalating issues via new response…. Feedback on Q2 Community Roadmap. Technical site integration observational experiment live on Stack Overflow.I have a similar task to do of parsing a log file but without understanding how this example works I wont be able to do anything.

I'd recommend you starting bu reading the documentation about the philosophy behind NiFi as well as the documentation of each processor you are mentioning. This will explain you the concept of flow files, repository, flows, content vs attributes, etc. View solution in original post. GenerateFlowFile processor document is saying generates files with random data, but no where in the processor configuration are the fields mentionedso where do we tell or how do we know what fields this file has?

Flow files are made of 'attributes' and 'content'. GenerateFF generates random flow files with content or not if you don't want to.

This is generally used to generate data to make start your flow but also mainly used for demonstration and test purpose. The ReplaceText processor only replaces content and is not modifying the attributes. Why five processors, simply to have generated the different part of the simulated logs you want to process.

Just have a look at the configuration of each processor.

Subscribe to RSS

You can also start a processor but not starting the next one in the flow. This will queue up flow files in the relationship. By right clicking on the relation, then lgoing to list, you will be able to see properties of each flow files as well as content. I'm sure this will help you understand the why and how. I looked at the configuration of the five processors and they look exactly same. As I said you can see what is generated by starting a processor to have flow file generated but not consumed by the next processor.

The GenerateFF only generates what we call core attributes such as UUI to uniquely identify a flow filefilename, path, etc. For the purpose of the tutorial we want to generate random logs from different countries, hence the multiple processors.

nifi generate flowfile example

Thanks Pierre now its beginning to make some senseso the 5 GenerateFF processors are there to take care of the 5 countries I guess. I want to read my own log filewhich processor would I use? I want to start with a simple task as read my log fileparse out some values by using the Regexp language and then save the parsed values to HIVE.

Support Questions. Find answers, ask questions, and share your expertise. Turn on suggestions. Auto-suggest helps you quickly narrow down your search results by suggesting possible matches as you type.

Showing results for. Search instead for. Did you mean:. Alert: Welcome to the Unified Cloudera Community. Former HCC members be sure to read and learn how to activate your account here.This processor creates FlowFiles with random data or custom content.

GenerateFlowFile is usefulfor load testing, configuration, and simulation. In the list below, the names of required properties appear in bold. Any other properties not in bold are considered optional. The table also indicates any default values, and whether a property supports the NiFi Expression Language.

nifi generate flowfile example

Dynamic Properties allow the user to specify both the name and value of a property. If Expression Language is used, evaluation will be performed only once per batch of generated FlowFiles. Supports Expression Language: true. If true, each FlowFile that is generated will be unique. If false, a random value will be generated and all FlowFiles will get the same content but this offers much higher throughput.

Finally, if Expression Language is used, evaluation will be performed only once per batch of generated FlowFiles Supports Expression Language: true. Specifies the character set to use when writing the bytes of Custom Text to a flow file. Specifies an attribute on generated FlowFiles defined by the Dynamic Property's key and value.These can be thought of as the most basic building blocks for constructing a DataFlow. At times, though, using these small building blocks can become tedious if the same logic needs to be repeated several times.

To solve this issue, NiFi provides the concept of a Template. A Template is a way of combining these basic building blocks into larger building blocks. Once a DataFlow has been created, parts of it can be formed into a Template.

This Template can then be dragged onto the canvas, or can be exported as an XML file and shared with others. Templates received from others can then be imported into an instance of NiFi and dragged onto the canvas. For more information on Templates, including how to import, export, and work with them, please see the Template Section of the User Guide. Here, we have a collection of useful templates for learning about how to build DataFlows with the existing Processors.

Please feel free to add any useful templates below. This example flow takes advantage of NiFi's ability to stream its own provenance data through the flow which it can then read, write, route, and transform for some interesting cases.


The provenance data it generates then becomes its own stream to experiment with. To do this we take advantage of the site to site reporting task for provenance, the new QueryRecord processor powered by Apache Calcite, various record readers and writers including a custom one built on the fly using Groovy all to read in the provenance stream while simultaneously writing it out in JSON, Avro, CSV, and XML. All this really helps highlight the power this makes available as we get to reuse the same components but plugin in separate concerns such as formats and schemas which we can reuse at various points in the flow.

Another great benefit here is we don't have to split data into individual records before the processors can operate on them. Through the record abstraction it understands how to parse, demarcate, and reproduce the transformed output without this having to be a concern of the user or the processors themselves.

This creates tremendous reuse, flexibility, and massive performance benefits all while still maintaining full fidelity of the provenance trail! NiFi status history is a useful tool in tracking your throughput and queue metrics, but how can you store this data long term?

This template works in tandem with the new SiteToSiteStatusReportingTask to automatically pipe status history metrics to an external Elasticsearch instance. This flow shows how to index tweets with Solr using NiFi.

Pre-requisites for this flow are NiFi 0. It then manipulates the data and writes it to a directory. Tails the nifi-app and nifi-user log files, and then uses Site-to-Site to push out any changes to those logs to remote instance of NiFi this template pushes them to localhost so that it is reusable.

A second flow then exposes Input Ports to receive the log data via Site-to-Site. Then data is then aggregated until the data for a single log is in the range of MB or 5 minutes passes, which occurs first.

The aggregated log data is then pushed to a directory in HDFS, based on the current timestamp and the type of log file e. The data is pulled in 1, records at a time and then split into individual records. NOTE: In order to use this template, there are a few pre-requisites. First, you need a table created in HBase with column family 'cf' and table name 'Users' This can be done in HBase Shell with the command: create 'Users', 'cf'. After adding the template to your graph, you will need to configure the controller services used to interact with HBase so that they point to your HBase cluster appropriately.

You will also need to create a Distributed Map Cache Server controller Service all of the default values should be fine. Finally, each of the Controller Services needs to be enabled.The intent of this Developer Guide is to provide the reader with the information needed to understand how Apache NiFi extensions are developed and help to explain the thought process behind developing the components.

It provides an introduction to and explanation of the API that is used to develop extensions. It does not, however, go into great detail about each of the methods in the API, as this guide is intended to supplement the JavaDocs of the API rather than replace them. This guide also assumes that the reader is familiar with Java 7 and Apache Maven. This guide is written by developers for developers. It is expected that before reading this guide, you have a basic understanding of NiFi and the concepts of dataflow.

NiFi provides several extension points to provide developers the ability to add functionality to the application to meet their needs. The following list provides a high-level description of the most common extension points:. The Processor interface is the mechanism through which NiFi exposes access to FlowFile s, their attributes, and their content. The Processor is the basic building block used to comprise a NiFi dataflow.

This interface is used to accomplish all of the following tasks:. The ReportingTask interface is a mechanism that NiFi exposes to allow metrics, monitoring information, and internal NiFi state to be published to external endpoints, such as log files, e-mail, and remote web services. An example use case may include loading a very large dataset into memory.

By performing this work in a ControllerService, the data can be loaded once and be exposed to all Processors via this service, rather than requiring many different Processors to load the dataset themselves. The FlowFilePrioritizer interface provides a mechanism by which FlowFile s in a queue can be prioritized, or sorted, so that the FlowFiles can be processed in an order that is most effective for a particular use case.

An AuthorityProvider is responsible for determining which privileges and roles, if any, a given user should be granted. The Processor is the most widely used Component available in NiFi. Processors are the only Component to which access is given to create, remove, modify, or inspect FlowFiles data and attributes. This means that all Processors must adhere to the following rules:.

This is a text file where each line contains the fully-qualified class name of a Processor. While Processor is an interface that can be implemented directly, it will be extremely rare to do so, as the org. AbstractProcessor is the base class for almost all Processor implementations. The AbstractProcessor class provides a significant amount of functionality, which makes the task of developing a Processor much easier and more convenient.

For the scope of this document, we will focus primarily on the AbstractProcessor class when dealing with the Processor API. NiFi is a highly concurrent framework. This means that all extensions must be thread-safe. If unfamiliar with writing concurrent software in Java, it is highly recommended that you familiarize yourself with the principles of Java concurrency. In order to understand the Processor API, we must first understand - at least at a high level - several supporting classes and interfaces, which are discussed below.

A FlowFile is a logical notion that correlates a piece of data with a set of Attributes about that data.

NiFi Developer’s Guide

While the contents and attributes of a FlowFile can change, the FlowFile object is immutable. Modifications to a FlowFile are made possible by the ProcessSession. The core attributes for FlowFiles are defined in the org. CoreAttributes enum. Filename filename : The filename of the FlowFile. The filename should not contain any directory structure. Absolute Path absolute. Discard Reason discard.

Alternate Identifier alternate.Apache NiFi offers a large number of components to help developers to create data flows for any type of protocols or data sources.

To create a flow, a developer drags the components from menu bar to canvas and connects them by clicking and dragging the mouse from one component to other.

Generally, a NiFi has a listener component at the starting of the flow like getfile, which gets the data from source system. On the other end of there is a transmitter component like putfile and there are components in between, which process the data. For example, let create a flow, which takes an empty file from one directory and add some text in that file and put it in another directory.

To begin with, drag the processor icon to the NiFi canvas and select GetFile processor from the list. Right-click on the processor and select configure.

Apache NiFi - Creating Flows

Go to settings tab, check the failure checkbox at right hand side, and then go back to the canvas. Go to settings tab and check the failure and success checkbox at right hand side and then go back to the canvas.

Now start the flow and add an empty file in input directory and you will see that, it will move to output directory and the text will be added to the file. By following the above steps, developers can choose any processor and other NiFi component to create suitable flow for their organisation or client.

Previous Page. Next Page. Previous Page Print Page.First of all I am a NiFi newbie! Our initial desire is to get a attribute from a flowfile and asks the SQLServer if this attribute exists. If the answer is YES, then the flowfile continue his way - but we didn't find anything like this.

But, again, we didn't find a processor that create a SQL Query dynamically. Is there any Processor that do this? Or we will have to create one?

Cause the examples that we find searching in internet only demonstrate how to replace text and using a lot of processors. Like the two below:. Any help will be appreciated! Hello Gabriel Queiroz. I hope I understood your use-case properly.

View solution in original post. Than you so much kkawamura!!! Glad to hear that works! A FlowFile has two different data, Attributes and Content. Content is a opaque binary data, and Attributes is something like a hash map having string keys and values META data. By using EL, you can construct a string value e. I also recommend reading EL Guide [2]. Thank you kkawamura. Support Questions. Find answers, ask questions, and share your expertise. Turn on suggestions. Auto-suggest helps you quickly narrow down your search results by suggesting possible matches as you type.

Showing results for. Search instead for. Did you mean:. Alert: Welcome to the Unified Cloudera Community. Former HCC members be sure to read and learn how to activate your account here. All forum topics Previous Next. Labels: Apache NiFi. Hi all, First of all I am a NiFi newbie! Reply 8, Views. Tags 2.

SDLC with Apache NiFi

Accepted Solutions. Hello Gabriel Queiroz I hope I understood your use-case properly. Reply 2, Views. I am so grateful for your answer! You removed tons of my back!