Mark,That's great! Unfortunately, though, no mind reading :) This is an extremely powerful pattern, though, thatfits perfectly within NiFi's wheelhouse. In a more generic sense, what we're really doing can be thought ofas registering a "streaming query." We can register a query for any text that matches a regular expression,or any text that begins with a certain string, etc. Then, data just streams through the Processor and goeswherever it is configured to go.Similarly, we can use EvaluateJsonPath in conjunction with RouteOnAttribute to do the same thing with JSONdata. Or EvaluateXPath & RouteOnAttribute to create a streaming query against XML. I believe something isin the works for Avro, as well.This ability to take in a flood of data, configure a few "queries" and have that flood of data separated out intowell-organized streams is probably my favorite aspect of NiFi. That being said, we are always learning andadapting and still reasonably new to the Open Source world. So feel free to critique and provide any feedbackyou have. Since this processor hasn't been officially released yet, this is the easiest time to affect large changes :)On Nov 13, 2015, at 6:46 PM, Mark Petronic <email@example.com> wrote:Got to say, Mark... Loving the RouteText processor!!! It definitely solved multiple tasks including, as a side effect, stripping the CSV header (because it does not match the regex) which I was doing before with a sed command. :) Thank you (and other contribs) for reading my mind and building this processor. Is that a feature of Nifi? Processors that show up before you ask for them? LOL. I was re-reading the developer guide and happened to see...
"For example, imagine that we want to have a RouteCSV Processor such that it is configured with multiple Regular Expressions."LOL. Guess someone had the baby in mind for awhile, too. :)On Fri, Nov 13, 2015 at 12:57 PM, Mark Petronic <firstname.lastname@example.org> wrote:Excellent! I will build and play ASAP. :)On Fri, Nov 13, 2015 at 12:25 PM, Mark Payne <email@example.com> wrote:Mark,Ok thanks for the more detailed explanation. I think that makes RouteText a much more appealing solution. It is available on master now.Thanks-Mark
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On Nov 13, 2015, at 12:12 PM, Mark Petronic <firstname.lastname@example.org> wrote:Thank you, Mark, for the quick reply. My comments on your comments..."That's a great question! 200 million per day equates to about 2K - 3K per second."Unfortunately, the rate will be much more extreme. Those records all show up over the course of about 4 hours. So, every time a new zip file appears on my NFS share, I grab it and process it. About 160 files will appear over those 4 hours. For example, the average sized zip file would likely contain about 180,000 or 1,600,000 records to process in that one scheduled Nifi run. And, it is very likely that, for a given run, say scheduled to run every 30 minutes, there could be multiple files to process. I did not mention it before but there are really two types of very large CSV files I have to process here, one is 18M records and the other 200M records per day. So, traffic is very bursty.Does that change anything regarding the intended use case for the new RouteText"We should have a RouteCSV processor as well."That would be very nice and definitely more performant without the need for regex matching. However, I definitely would benefit even from RouteText, in the interim. For my use case, the regex will be pretty simple as the timestamp is close to the front of the record, but I see where you are going on the potential complexity with groups and widely spread out fields of interest.Is RouteText available on any branch where I could build and play around with it before 0.4.0?Thanks,MarkOn Fri, Nov 13, 2015 at 11:14 AM, Mark Payne <email@example.com> wrote:Mark,That's a great question! 200 million per day equates to about 2K - 3K per second. So that is quite reasonable.You are very correct, though, that splitting that CSV into tons of one-line FlowFiles does indeed have a cost.Specifically, the big cost is the Provenance data that is generated at that rate. But again 2K - 3K per secondgoing through a handful of Processors is a very reasonable workload.I will caution you, though, that there is a ticket  where people will sometimes run into Out Of Memory Errorsif they try to split a huge CSV into individual FlowFiles because it holes all of those FlowFile objects (not thedata itself but the attributes) in memory until the session is committed. The workaround for this (until that ticketis completed) is to use a SplitText to split into 10,000 lines or so per FlowFile and then another SplitText tosplit each of those smaller ones into 1-line FlowFiles.Also of note, in 0.4.0, which is expected to be released in around a week or so, there is a new RouteTextProcessor. This, I think, will make your life far easier. Rather than using SplitText, Extract Text, and MergeContentin order to group the text, RouteText will allow you to supply a Grouping Regex. So that regex can just pull out thedevice id, year, month, and day, from each line and group together lines of text that have the same values intoa single FlowFile. For instance, if your CSV looked like:# device_id, device_manufacturer, value, year, month, day, hour1234, Famous Manufacturer, 83, 2015, 11, 13, 12You could define a grouping regex as:(\d+), .*?, .*?, (\d+), (\d+), (\d+), .*It looks complex but it's just breaking apart the CSV into individual fields and grouping on device_id, year, month, day.This will also create a RouteText.Group attribute with the value "1234, 2015, 11, 13"This processor provides two benefits: it combines all of the grouping into a single Processor, and it cuts down on themillions of FlowFiles that are generated and then merged back together.As I write this, though, I am realizing that the regex above is quite a pain. We should have a RouteCSV processor as well.Though it won't provide any features that RouteText can't provide, it will make configuration far easier. I created a ticketfor this here . I'm not sure that it will make it into the 0.4.0 release, though.I hope this helps!Thanks-MarkOn Nov 13, 2015, at 10:44 AM, Mark Petronic <firstname.lastname@example.org> wrote:I have a concept question. Say I have 10 GB of CSV files/day containing records where 99% of them are from the last couple days but there are stragglers that are late reported that can date back many months. Devices that are powered off at times don't report but eventually do when powered on and report old, captured data - store and forward kind of thing. I want to capture all the data for historic reasons. There are about 200 million records per day to process. I want to put this data in Hive tables that are partitioned by year, month, and day and use ORC columnar storage. These tables are external Hive tables and point to the directories where I want to drop these files on HDFS, manually add new partitions, as needed, and immediately be able to query using HQL.Nifi Concept:0. Use GetText to get a CSV file1. Use UpdateAttribute to parse the incoming CSV file name to obtain a device_id and set that as an attribute on the flow file2. Use SplitText to split each row into a flow file.3. Use ExtractText to identify the field that is the record timestamp and create a year, month, and day attribute on the flow file from that timestamp.4. Use MergeContent with a grouping key made up of (device_id,year,month,day)5. Convert each file to ORC (many Parquet). This stage will likely require me building a custom processor because the conversion is not going to be a simple A-to-B. I want to do some validation on fields, type conversion, and other custom stuff against some source-of-truth schema stored in a web service with REST API.6. Use PutHDFS to store these ORC files in directories like .../device_id/year=2015/month=11/day=9 by using the attributes already present from the upstream processors to build up the path, where device_id is the Hive table name and the year, month, day are the partition key name=value per Hive format. The file names will just be some unique ID, they don't really matter7. Use ExecuteProcessStream to execute a Hive script that will "alter table add partitions...." for any partitions that were newly created on this schedule runIs this insane or is it what Nifi was designed to do? I could definitely see using a Spark job to do the group by (device_id,year,month,day) stage. 200M flow files from the SplitText is the one that has me wondering if I am nuts thinking of doing that? There must be overhead on flow files and deprecating them to one line each seems to me as a worst case scenario. But it all depends on the core design and whether Nifi is optimized to handle such a use case.Thanks,Mark