intToRational is only in the scope of Rational in your code, so the conversion isn't available to Ints outside of the Rational class -- the reverse order (x + 2) works because 2 ends up being bound by the + inside Rational, where the conversion is available. What you want to do is create a companion Rational object, define intToRational there, and then you can import it in Rational, and outside of it (global scope, e.g.) too. As a minor note, I'd check out the Spire project for a complete set of rational classes, plus a whole lot more. ... View more

@Raj B The SplitText processor has a "Header Line Count" property. If you set this to 1, you should be able to achieve what you want in generating multiple flow files, each with the same header. That said, if you're intending to insert these into Hive, you could actually use ConvertCSVToAvro too, setting the delimiter to '|' and then you'd have the data in batches which should give you better throughput. ... View more

@Randy Gelhausen There are a few ways to do this. Use the distributed map cache to get runtime attribute lookups and re-populate it as needed with new configs. Use a scripted processor to lookup your config values and merge the attributes on to the FlowFile. I have some work in progress extending a lookup table service by @Andrew Grande that can do lookups against a properties file that is reloaded periodically. It includes a LookupAttribute processor that can merge in either specific properties or all the properties from a properties file. See: https://github.com/jfrazee/nifi-lookup-service/tree/file-based-lookup-service ... View more

@Pedro Rodgers Three things jump out at me: There are a ton of examples in your sample data with just one or two occurrences of the pattern. For this particular algorithm and its usual application they're not going to be very useful. Your confidence is quite high, considering the size of the sample data and the evidence for the different patterns. The learner came up with 1165 rules for 1185 data points. I re-ran your code including a .filter(_._2 > 2) after the .reduceByKey(_ + _) and lowered the confidence to 0.6 and I get 20 or so rules now with confidence varying between 0.6 and 1.0. I suspect if you carefully go through the results you were getting before you might see that it was just learning a 1-to-1 mapping between input and output, so the confidence of 1.0 is justified, but the generalization of the model is bad. ... View more