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Note that your password must be the root directory of Nifi when you run this command. Note : Config option should have path to the alphavantage. Choose the table that you would like to ingest incrementally. Choose the controller service where you configured Hive Connection in previous steps.
To start the flow, right click every processor and select Start. If you have scheduled the QueryDatabasetable to run after an elapsed time, confirm that the fetch incremental data was pulled from the REST API and was ingested into Hive automatically. Feel free to try the connector with Nifi or any application you want. If you have any questions or issues, please contact us or comment below.
All Rights Reserved. See Trademarks for appropriate markings. Services Consulting Education Modernization Outsourcing. Back to top. Download and install Apache Nifi on your machine. Install the connector by running the setup executable file on your machine and following the instructions on the installer.
Information" : "VarChar 84 ". Symbol" : "VarChar 64key". Last Refreshed" : "Timestamp 0 ".
Interval" : "VarChar 64 ". Output Size" : "VarChar 64 ". Time Zone" : "VarChar 64 ". You should see a canvas as shown below when the page loads. To start building the flow, begin with configuring the JDBC drivers. On your canvas, there are two side bars; Navigate and Operate.NET developer.
Hello, i have a question. I have a requirement to run a dynamic query in Hive. Can i achive that using executesql processor? Please help me. What do you mean by a dynamic query? Hey Matt, I am actually trying to transfer.
Hi Matt: I am new to NiFi. Any help or examples greatly appreciated. Hey great post I am trying to do the same type of thing with HBase but get Caused by: groovy. MissingMethodException: No signature of method: com. I am trying to implement increment method reusing HBase service. Which part are you having trouble with? Have you seen the documentation for the controller service? ProcessExecption: org. GroovyRuntimeException: Ambiguous method overloading for method groovy. Cannot resolve which method to invoke for [null] due to overlapping prototypes between: [interface javax.
Implement a DBCPConnectionPool that dynamically selects a connection pool
DataSource] [interface java. You can remove the question mark from the above line to see if you get a NullPointerException when calling getConnection. If so, then you did not find the Controller Service you were looking for.
Hi Matt, Thanks for your post, I followed your instructions exactly but for some reason, flow works for few hours then I get connection problem.It turned out to be very easy and not really any different from a JDBC compliant database, but at the same time frustrating enough to make me post about it, hoping it will save someone's time.
In the archive you will see a very nice document, describing all the JDBC parameters, with lots of examples. Then you have a choice to use JDBC 4 or 4. In the 4. Again, it is important, that an account NiFi is running under, has permissions to access that location. Connection pool controller service stores conveniently JDBC connection details, and also serves connections from the pool and allows to limit concurrent connections to your source system. It stores encrypted password as well:.
And this is when I got frustrated. I tried a bunch of various JDBC connection string settings and I even tried to use Hive connector instead a little known fact, Hive connector can be used to connect to Impala just fine - nothing helped. And guess what? I restarted NiFi and everything started working magically. Now that I think about it, it makes sense. Maybe it is a common knowledge to Java developers, but this has caused 4 hours wasted for me.
The old Microsoft Windows trick - restart your system if nothing works - actually works :. Did you know that you can access Impala and run queries from ExecuteScript and InvokeScriptedProcessor processors, using your connection pool controller service? Head over to Matt Burgess's blog to learn how. Thanks to Matt, I picked up some basic Groovy. I was not planning to learn a new language but Groovy turned out to be pretty awesome and handy with NiFi.
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've set the Driver Class name as:. ProcessException: org. No FlowFile to route to failure: org. Learn more.
How to connect Apache NiFi to Apache Impala
Asked 2 years, 4 months ago. Active 2 years, 4 months ago. Viewed times. I've set the Driver Class name as: com. Joe Dankers. Joe Dankers Joe Dankers 21 3 3 bronze badges. Did you set property Database Driver Location s? What version of NiFi are you using? Also are all the dependencies in a flat directory with the driver, or are there sub folders?
Active Oldest Votes. Really strange. Thanks anyway! Restarted Nifi service and connection started to work out of the blue. 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. Q2 Community Roadmap. The Unfriendly Robot: Automatically flagging unwelcoming comments. 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.Thank you so much for this nice information. Hope so many people will get aware of this and useful as well. And please keep update like this.
Change data capture CDC is a notoriously difficult challenge, and one that is critical to successful data sharing. To that end, a number of data flow vendors have proprietary CDC solutions, each of which is very expensive to purchase, support and operate. For the cost conscious enterprise, there is a viable and robust alternative which costs nothing to purchase and which has relatively low support and operating expenses: Apache NiFi.
In the CDC shadow table are the columns from the source table as well as additional columns which hold CDC metadata, e. The notoriously difficult part of the challenge is keeping downstream target RDBMS tables synchronized with the contents to the source CDC shadow table as per the service level agreement SLAgiven disk failures, network outages, and other types of inevitable events which invariably impede data flows.
At its creation inthe National Security Agency NSA put everything possible into NiFi to make it the best and most useful data platform, on the globe. As such, you can depend on NiFi for superior service over the long haul. However, unlike most Apache projects, NiFi is an appliancea highly secure, and very easy to use appliance. The NiFi UI is web browser based. To build a data flown using NiFi, you simply drag and drop any 1 of over processors onto the canvas.
Database migration using Apache NiFi
For each processor icon on the canvas, you fill in the mandatory property values, and link the processor to other processors as needed. Which processors you choose to use is specific to your business use case. Since Apache NiFi supports all 4 enterprise integration patterns:.
As with any data flow, at any step in the flow, problems can occur, and so the PutFile processor is used to log those issues to disk. To handle that scenario, you need to first now how many records are in the query result set. NiFi has 2 types of data flow files: one holds metadata about the flow, and the other holds the actual data content.
The QueryDatabaseTable output has metadata about the count of records in the result set. The RouteOnAttribute processor uses the row count flow metadata attribute to determine if the query result set needs to be split.
This Groovy script copies values of elements in the JSON string and appends them as new attributes, new metadata fields, of the flow. Of course, there are other scenarios which arise within a CDC data flow, and which must be handled by a production solution.
The design described above is a generic template, not a universal solution to all CDC scenarios. However, the abundance of existing drag and drop NiFi processors enable easy development of more complex CDC scenarios then described above. As regards ExecuteScript, it supports not just Groovy which I prefer with NiFi given its integration with Javabut other script languages as well, e.How to call MS SQL server from Hortonworks NiFi (running on Docker)
Lastly, though this generic template contains all of the processors running in a single NiFi canvas, the processors can be distributed as well. For a demonstration of these CDC capabilities of Apache NiFi, or to investigate the significant cost savings delivered by Apache NiFi, just send an email to charles thesolusgroupllc. Edward December 16, at AM. Newer Post Older Post Home. Subscribe to: Post Comments Atom.Apache NiFi is a great tool for building flexible and performant data ingestion pipelines.
In this article we will look at Apache NiFi's built-in features for getting FlowFile data into your database, and some strategies you should consider in implementing a database flow. The concept is dead simple - we take incoming data records, do some processing on them, then insert them into our database.
Exactly the sort of thing you expect to do with NiFi. Things get a bit more complicated when you consider the stampede of data going through NiFi, and how that will translate to your RDBMS. Thankfully, NiFi has some good solutions.
PutSQL not only handles the basic mechanics of connecting to the database, it will also batch requests together for improved performance. By default, up to FlowFiles may be processed at a time.
In practice, I find these to be similar in that both require one or more processors upstream of PutSQL to marshal the FlowFile content and attributes into shape. Partly, I find it easier to format the FlowFile content rather than formatting several parameter attributes. But I really like this method for making it easier to troubleshoot. My typical PutSQL flows look something like this gist :.
Because the PutSQL processor attempts to batch statements together for optimal performance, the error messages returned from failed statements are not obvious or individualized to the failing FlowFile and its statement. Batch error messages look like this:.
Failed to update database due to a failed batch update. There were a total of 1 FlowFiles that failed, 2 that succeeded, and 5 that were not execute and will be routed to retry. In the case above, 8 FlowFiles went in to the processor, only 1 failed, but only 2 succeeded. The 5 FlowFiles that were not executed may be retried as-is, as long as you make sure to route the retry relationship back to PutSQL. But what should you do with these failures?
If you are in a development environment, or troubleshooting connectivity with test data, routing FlowFiles back to PutSQL can make sense. But in production, routing back to the processor won't help if the FlowFiles are certain to fail again. At the very least, you should have somewhere for them to go that allows you to spot the failures, understand the causes, and debug your data and flow.
One possible solution would be to route the FlowFiles out to disk files to be edited and reloaded. For flow application errors, a more NiFi solution is to fix the upstream cause and use the provenance data to replay the FlowFile through the fixed flow. This works great, but can be difficult to do in bulk.
I like the strategy above because it is fairly general-purpose. But there is always another way with NiFi, depending on what your data looks like. Some other good options include:. Many databases support bulk load capabilities outside the SQL standard, typically a command to load records from a text or XML file.
The flexibility of NiFi shines here, as it has many features for batching incoming data, form the necessary files, then triggering the load operation.
I'll try this out in a future post.