Nifi api port


  • Getting Started with Apache Nifi: Migrating from Relational to MarkLogic
  • Apache Nifi – Combining MySQL and PostgreSQL records over REST API
  • Apache NiFi API Remote Code Execution - Metasploit
  • How to Ingest Files and NetFlow with Apache NiFi
  • New Blog Post!
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  • Getting Started with Apache Nifi: Migrating from Relational to MarkLogic

    As possible type values we will expect csv of xlsx, so we will build our NiFi flow to handle either csv files or excel files. In both cases, we will return data in JSON format. The image below offers an overview of the complete flow. We already covered the configuration of the first and last processors the ones in yellow , so in the next sections we will go through the details of each of the remaining sections, where the API logic is implemented.

    Figure 5: Overview of the full NiFi flow 3. Fetch the file using the request parameters In this section in the picture, in red we split the flow depending on the type header csv or xlsx and we fetch the file with the specified path and name. The FlowFile coming from the Request handler has a series of attributes that describe the request. Amongst them, we can find http. They hold the values that we pass to the request in order to identify the file that we would like to fetch. They are visible in the picture below.

    This is not strictly needed, but it helps to properly extract the path from the request, especially if it comes in a more complex format.

    Of course, this is a very simplified case, but depending on how we configure it and which formulas of the NiFi Expression Language we use, we could tackle different scenarios with more complex paths or with different URI structures.

    This processor allows us to split the flow depending on which type of file we are looking to fetch, as they will have to be processed differently. As depicted in the picture below, this processor needs you to define the states you require and the rule to match the incoming FlowFile to one of them.

    We take the type header and we create an output state for the two options we expect: csv and xlsx. In the image below representing the configuration tab of both processors , you can see how the file is identified by a parametrized string, using the attributes that we described. Figure 9: FetchFile configuration tabs 4. We test it with two methods: with a Curl call and with a Postman call.

    First, make sure to start the flow. With the flow still running, change your query to fetch a csv file and set the proper header in the Postman request. You should get a similar result to the one in the below picture. However, the case we described is quite simple, and most probably not comparable to a real production case. Valerio d.

    Apache Nifi – Combining MySQL and PostgreSQL records over REST API

    Gabo Manuel The typical process of migrating data from a relational database into MarkLogic has always translated to ad-hoc code or csv dumps to be processed by the MarkLogic Content Pump mlcp. Apache NiFi introduces a code-free approach of migrating content directly from a relational database system into MarkLogic. Here we walk you through getting started with migrating data from a relational database into MarkLogic.

    Note: The following steps assume that MarkLogic v9 or higher is already running and is available locally. Additionally, Java 8 needs to be installed and configured. NiFi Setup Check out instructions on how to download a binary or build and run NiFi from source code , as well as a quick guide in getting it up and running. You might be surprised with a blank page on fresh start of the scripts, it takes a while to load.

    For additional information about the available processors, visit the Apache NiFi documentation. Defining the Flow We want to establish a basic flow with the following steps: Retrieve records from the relational database Convert each row into a json document Ingest data into MarkLogic NiFi Basics: How to add a processor To add a Processor to your dataflow canvas, drag the Processor icon from the top of the screen down to the canvas and drop it there: Figure 1: Adding a Processor to dataflow canvas This will generate a dialog box that prompts you to select the Processor to add see Figure 2.

    You can use the search box on the upper right to reduce the number of Processors displayed. Figure 2: Add Processor screen Flow Step 1: Retrieving relational data As shown in Figure 2, there are several processor options to retrieve data from a relational database. This is optimal for processing a table whose records never get updated e. This processor requires the user to supply the full SQL statement, giving room for de-normalizing the records at relational database level via joins.

    The example below is for a MySQL database running on my local machine. The default is to run almost instantaneously after the first execution completes. If you only want to run the process once, consider having a really high run schedule for timer driven strategy, or specifying a fixed CRON value for execution.

    Therefore, for consistency during re-runs, we recommend using the existing primary key as part of the resulting document URI. In order to do this, we extract the primary key and store it as part of the FlowFile attribute. The value of this property is a JsonPath expression to be evaluated. Click and drag this icon towards the ConvertAvroToJSON Processor and drop it there to make a connection: If the connection is successfully created, a connection configuration screen will display.

    You should now have a flow similar to this: Figure Dataflow canvas with Processors and connections Run it! Now you have your data migrated from your relational database into MarkLogic.

    Bring DHF into the Mix! A detailed guide on getting an Operational Data Hub is available to help you get started, along with the Data Hub QuickStart tutorial , which will walk you through getting your entities and input flows created. Note that you must disable the service to make any changes. Modify our PutMarkLogic Processor as follows. Not only have you migrated data from your relational database into MarkLogic, you can now harmonize your data before downstream systems can access it.

    Saving and Reusing a NiFi Template Wiring the processors took no time at all to implement, but doing it over and over again can become tedious and can easily introduce mistakes during execution. Select the components on the canvas you want to save as a template, including the connectors. Notice the darker blue borders.

    If successful, you will see a prompt like this: We can now make use of the template icon on the top. Click and drag it down to the canvas to get the following prompt: If you expand the drop down, you can hover over the question mark icon to show the description of the template.

    This is a good reason to be concise but descriptive when creating your templates. When you add the template to your existing canvas, the new processors may overlap on top of existing processors. Rearrange them as needed i. After adding this template, you may notice that the PutMarkLogic and SplitJson processors have yellow triangle alert icons because sources have not yet been specified.

    Note that the template creates a new MarkLogic DatabaseClient Service for the new processor instance created by the template. We have two options at this point: Delete this instance and select the existing and enabled instance.

    This is recommended if we will be using the same MarkLogic instance with the same credentials and transforms. Rename and enable this new instance. Additional Reading.

    Apache NiFi API Remote Code Execution - Metasploit

    You should now have a flow similar to this: Figure Dataflow canvas with Processors and connections Run it! Now you have your data migrated from your relational database into MarkLogic. Bring DHF into the Mix! A detailed guide on getting an Operational Data Hub is available to help you get started, along with the Data Hub QuickStart tutorialwhich will walk you through getting your entities and input flows created.

    Note that you must disable the service to make any changes. Modify our PutMarkLogic Processor as follows. Not only have you migrated data from your relational database into MarkLogic, you can now harmonize your data before downstream systems can access it. Saving and Reusing a NiFi Template Wiring the processors took no time at all to implement, but doing it over and over again can become tedious and can easily introduce mistakes during execution.

    Select the components on the canvas you want to save as a template, including the connectors.

    How to Ingest Files and NetFlow with Apache NiFi

    Notice the darker blue borders. If successful, you will see a prompt like this: We can now make use of the template icon on the top. Click and drag it down to the canvas to get the following prompt: If you expand the drop down, you can hover over the question mark icon to show the description of the template. This is a good reason to be concise but descriptive when creating your templates.

    When you add the template to your existing canvas, the new processors may overlap on top of existing processors. Rearrange them as needed i. After adding this template, you may notice that the PutMarkLogic and SplitJson processors have yellow triangle alert icons because sources have not yet been specified. Note that the template creates a new MarkLogic DatabaseClient Service for the new processor instance created by the template.

    We have two options at this point: Delete this instance and select the existing and enabled instance. This is recommended if we will be using the same MarkLogic instance with the same credentials and transforms. Rename and enable this new instance. Figure 2: To create a ParseNetflowv5 processor add it to the canvas and checkfailure and original in settings. Last, create a PutUDP processor to send the processed data out to a syslog server. Check success and failure under Automatically Terminate Relationships since this is the last processor in the flow.

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    Now go to Properties and add the syslog hostname and destination port. Figure 3: To create the PutUDP processor add the syslog hostname and destination port in Properties as the final steps. Now to connect the processors. Lastly, right-click the canvas and select Start.

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    The processor indicators should turn green. Figure 4: Process turn green when you finish set up. The data should now be showing up in your syslog server. Know there will be a lot of data on a busy network. This is easy using NiFi. Assume you have a CSV being dumped to a remote host every 15 minutes.


    Nifi api port