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On February 1, 2020 at 12:33:44, Emanuel Oliveira ([email protected]) wrote: Hi, Based on recent experience, I found very hard to implement logic which i think should exists out of the box, and instead it was slow process of keeping discovering a property on a processor only works for a type of data when processor supports multiple types etc. I would like you all to keep it simple attitude and imagine hwo you would implement a basic scenario as: *basic scenario 1 - shall be easy to implement out of the box following 3 needs:* CSV (*get schema automatically via header line*) --> *validate mandatory subset of fields (presence) and (data types)* --> *export subset of fields* or all (but want some of them obfuscated) problems/workarounds found 1.9 rc3 *1. processor ValidateRecord* [1.1] *OK* - allows *getting schema automatically via header line* and *mandatory subset of fields* (presence) via the 3 schema properties --> suggest rename properties to make clear those at processor level are "mandatory check" vs the schema on reader which is the well the data read schema. [1.2] *NOK* - does not allow *types validation**.* *One could thinking using InferSchema right ? problem is it only supports JSON.* [1.2] *NOK* - ignores writer schema where one could supply *subset of original fields* (always export all original fields) --> add property to control export all fields (default) or use writer schema(with subset). *2. processor ConvertRecord* [2.1] *OK* csvreader able to *get schema from header -*-> maybe improve/add property to cleanup fields (regex search/replace - so we can strip whitespaces and anything else that breaks nifi processors and/or that doesnt interest us) [2.2] *NOK* missing *mandatory subset of fields.* [2.3] *OK* but does good jobs converting between formats, and/or *export all or subset of fields via writer schema*. *3. processor InferAvroSchema* [3.1] NOK - despite property "Input Content Type" lists CSV, JSON as inbound data, in reality the property "Number Of Records To Analyze" only supports JSON. Took us 2 days debugging to understand the problem.. 1 CSV with 4k lines and mostly nulls, "1"s or "2"s but some few records would be "true" or "false".. meaning avro data type should have been [null, string] but no.. as we found out, type kept being [null, long] with doc always using 1st data line in CSV to determine field type. This was VERY scaring to find out.. how can it be this was fully working as expected ? We endup needing to add +1 processor to convert CSV into JSON so we could get proper schema.. and even now we still testing, as seems all fields got [string] when some columns should be long. Im not sure the best way to expose this, but im working at enterprise level, and believe me, this small but critical nuances are starting to push the mood on NiFi. But because I felt in love with NiFi and i like the idea of graphical design of flows etc, but we really must fix this critical little devils.. they are being screamed as nifi problems at management level. I know nifi is open source, and its upon us developers to improve, i just would like to call attention that we must be sure on the middle of PRs and JIRA enhancements we not forgetting the basic threshold.. doesn't make sense to release a processor with only 50% of its main goal developed when the remaining work would be easy and fast to do (aka InferAvroSchema). As i keep experimenting more and more with NiFi, i start detecting the level of basic quality features is bellow from what i think it should be. Better not release incomplete processors at least regarding core function of the processor. I know developers can contributes with new code, fixes and enhancements.. but is there any gatekeeper team double checking the deliverables ? like at basic developer should provide enough unite tests.. again the InferAvroSchema being a processor to export avro schema based on either a CSV or JSON, then obviously there should be couple unit testings CSVs and JSON with different data so we can be sure sure we have the proper type on the avro schema exported right ? Above i share some ideas, and i got much more from my day by day experience that i been working with NiFi at entperise level for more than 1 year by now. Let me know what shall be the way to create JIRAs to fix several processors in order to allow aone unexperienced nifi client developer to accomplish the basic flow of: CSV (*get schema automatically via header line*) --> *validate mandatory subset of fields (presence) and (data types)* --> *export subset of fields* or all (but want some of them obfuscated) I challenge anyone to come out with flows to implement this basic flow.. and test and see what i mean,, you will see how incomplete and hard are things.. which should not be the case at all. NiFi shall be true Lego, add processors that says does XPTO and trust it will.. but we keep finding a lot of nuances.. I dont mind taking 1 day off my and work have a meeting with some of you - dont know if theres such a thing as tech lead on nifi project? - and i think would be urgent to fix the foundations of some processors. Let me know.. Best Regards, *Emanuel Oliveira*
