So I'm a complete rookie with NiFi and when I was trying it out for the first time, I just ran a single "GetFile" processor and set it to a fairly important directory, and now all of the files are gone. I poked around in the Content Repository, and it would appear that there are a whole lot of files there that are in some unknown format. I am assuming those are the files from my HD, but are now in "FlowFile" format. I also noticed that I can look at the provenance records and download them one by one, but there are several thousands...so that is not an option.
So if I'm looking to restore all of those to those files, I imagine I would need to read all of those in the content repository as flowfiles, and then do a PutFile. Any suggestions on how to go about this? Thanks so much!
If you still have the flowfiles in a queue, add a PutFile processor to another directory (not your important one) and move the queue over to it (click the queue that has the flowfiles in it and drag the little blue square at the end of the relationship over to the new PutFile). Run the PutFile and let it drain out. The files might not come out like-for-like, but the data will be there (assuming you didnt drop any flowfiles).
Don't develop flows on important directorties that you don't have backups for. Copy a data subset to a testing dir.
trying to record a relatively lengthy video (7-8 hours) using k4arecorder.exe. Have tried to split it up into hourlong recordings, or record it in one go. It will occasionally record fine, but will randomly crash with the following error messages included in the screenshot. Sometimes this occurs at 15 minutes, sometimes after 5 hours, sometimes not at all. Can anyone explain this error to me?
errors are:
releasing capture early due to full queue T5
The cluster containing the timestamp -XXXX has already been written to disk.
Returned failure in k4a recurd write capture ()
screenshot
Fairly new to using nifi. Need help with the design.
I am trying to create a simple flow with dummy csv files(for now) in HDFS dir and prepend some text data to each record in each flowfile.
Incoming files:
dummy1.csv
dummy2.csv
dummy3.csv
contents:
"Eldon Base for stackable storage shelf, platinum",Muhammed MacIntyre,3,-213.25,38.94,35,Nunavut,Storage & Organization,0.8
"1.7 Cubic Foot Compact ""Cube"" Office Refrigerators",BarryFrench,293,457.81,208.16,68.02,Nunavut,Appliances,0.58
"Cardinal Slant-D Ring Binder, Heavy Gauge Vinyl",Barry French,293,46.71,8.69,2.99,Nunavut,Binders and Binder Accessories,0.39
...
Desired output:
d17a3259-0718-4c7b-bee8-924266aebcc7,Mon Jun 04 16:36:56 EDT 2018,Fellowes Recycled Storage Drawers,Allen Rosenblatt,11137,395.12,111.03,8.64,Northwest Territories,Storage & Organization,0.78
25f17667-9216-4f1d-b69c-23403cd13464,Mon Jun 04 16:36:56 EDT 2018,Satellite Sectional Post Binders,Barry Weirich,11202,79.59,43.41,2.99,Northwest Territories,Binders and Binder Accessories,0.39
ce0b569f-5d93-4a54-b55e-09c18705f973,Mon Jun 04 16:36:56 EDT 2018,Deflect-o DuraMat Antistatic Studded Beveled Mat for Medium Pile Carpeting,Doug Bickford,11456,399.37,105.34,24.49,Northwest Territories,Office Furnishings,0.61
the flow
splitText-
ReplaceText-
MergeContent-
(this may be a poor way to achieve what I am trying to get, but I saw somewhere that uuid is best bet when it comes to generating unique session id. So thought of extracting each line from incoming data to flowfile and generating uuid)
But somehow, as you can see the order of data is messing up. The first 3 rows are not the same in output. However, the test data I am using (50000 entries) seems to have the data in some other line. Multiple tests show usually the data order changes after 2001st line.
And yes, I did search similar issues here and tried using defragment method in merge but it didnt work. I would appreciate if someone can explain what is happening here and how can I get the data in the same way with unique session_id,timestamp for each record. Is there some parameter I need to change or modify to get the correct output? I am open to suggestions if there is a better way as well.
First of all thank you for such an elaborate and detailed response. I think you cleared a lot of doubts I had as to how the processor works!
The ordering of the merge is only guaranteed in defragment mode because it will put the flow files in order according to their fragment index. I'm not sure why that wouldn't be working, but if you could create a template of a flow with sample data that showed the problem it would be helpful to debug.
I will try to replicate this method using a clean template again. Could be some parameter problem and the HDFS writer not able to write.
I'm not sure if the intent of your flow is to just re-merge the original CSV that was split, or to merge together several different CSVs. Defragment mode will only re-merge the original CSV, so if ListHDFS picked up 10 CSVs, after splitting and re-merging, you should again have 10 CSVs.
Yes, that is exactly what I need. Split and join data to their corresponding files. I dont specifically (yet) need to join the outputs again.
The approach of splitting a CSV down to 1 line per flow file to manipulate each line is a common approach, however it won't perform very well if you have many large CSV files. A more efficient approach would be to try and manipulate the data in place without splitting. This can generally be done with the record-oriented processors.
I used this approach purely instinctively and did not realize this is a common method. Sometimes the datafile could be very large, that means more than a million records in a single file. Wont that be an issue with the i/o in the cluster? coz that would mean each record=one flowfile=one unique uuid. What is a comfortable number of flowfiles that nifi can handle? (i know it depends on cluster config and will try to get more info about the cluster from hdp admin)
What do you suggest by "try and manipulate the data in place without splitting" ? can you give an example or template or processor to use?
In this case you would need to define a schema for your CSV which included all the columns in your data, plus the session id and timestamp. Then using an UpdateRecord processor you would use record path expressions like /session_id = ${UUID()} and /timestamp = ${now()}. This would stream the content line by line and update each record and write it back out, keeping it all as one flow file.
This looks promising. Can you share a simple template pulling files from hdfs>processing>write hdfs files but without splitting?
I am reluctant to share the template due to restrictions. But let me see if I can create a generic templ and I will share
Thank you for your wisdom! :)
A tool I'm writing is responsible for downloading thousands of image files over a matter of many hours. Originally, using TIdHTTP, I would Get the file(s) into a TMemoryStream, and then save that to a file, so long as there were no exceptions. In order to improve speed, I changed the TMemoryStream to a TFileStream.
However, now if the resource was not found, or otherwise any sort of exception which results in no actual file, it still saves an empty file.
Completely understandable, since I simply create a file stream just prior to the download...
FileStream:= TFileStream.Create(FileName, fmCreate);
try
Web.Get(AURL, FileStream);
finally
FileStream.Free;
end;
I know I could simply delete the file if there was an exception. But it seems far too sloppy. I'm sure there's a more appropriate method of aborting such a situation.
How should I make this to not save a file if there was an exception, while not altering the performance (if at all possible)?
How should I make this to not save a file if there was an exception, while not altering the performance (if at all possible)?
This isn't possible in general. Errors and failures can happen at any step if the way, including part way through the download. Once this point is understood, then you must accept that the file can be partially downloaded and then abandoned. At which point where do you store it?
The obvious choices are memory and file. You don't want to store to memory, which leaves to file.
This takes you back to your current solution.
I know I could simply delete the file if there was an exception.
This is the correct approach. There are a few variants on this. For instance you might download to a temporary file that is created with flags to arrange its deletion when closed. Only if the download completes do you then copy to the true destination. This is the approach that a browser takes. But the basic idea is to download to file and deal with any failure by tidying up.
Instead of downloading the entire image in one go, you could consider using HTTP range requests if the server supports it. Then you could chunk the file into smaller parts, requesting the next part after the first finishes (or even requesting multiple parts at the same time to increase performance). If there is an exception then you can about the future requests, so they never start in the first place.
YouTube and a number of streaming media sites started doing this a while ago. It used to be if you started playing a video, then paused it, then it would eventually cache the entire video. Now it only caches a little ahead of the current position. This saves a ton of bandwidth because of the abandon rate for videos.
You could write the partial file to disk or keep it in memory.
I am using Intellirace to get production error in the itrace log file. Client is having concern doing so, once concern is the client data (like the SSN , Name, Telephone numbers etc. Is there any way i can mask the client data from the intrellrace .itrace log file before using it for historic debugging.
Masking certain data in .ITrace file currently isn't a supported scenario.
On the other hand, IntelliTrace won't collect raw memory and only collect limited data in call stack. Even under "call and event" mode it collects parameters but won't go deeper than one level. IntelliTrace file is less likely to carry sensitive data comparing to traditional dump files.