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 <markpetro...@gmail.com> wrote: > Excellent! I will build and play ASAP. :) > > On Fri, Nov 13, 2015 at 12:25 PM, Mark Payne <marka...@hotmail.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 >> >> Sent from my iPhone >> >> On Nov 13, 2015, at 12:12 PM, Mark Petronic <markpetro...@gmail.com> >> 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, >> Mark >> >> On Fri, Nov 13, 2015 at 11:14 AM, Mark Payne <marka...@hotmail.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 second >>> going through a handful of Processors is a very reasonable workload. >>> >>> I will caution you, though, that there is a ticket [1] where people will >>> sometimes run into Out Of Memory Errors >>> if they try to split a huge CSV into individual FlowFiles because it >>> holes all of those FlowFile objects (not the >>> data itself but the attributes) in memory until the session is >>> committed. The workaround for this (until that ticket >>> is completed) is to use a SplitText to split into 10,000 lines or so per >>> FlowFile and then another SplitText to >>> split 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 RouteText >>> Processor. This, I think, will make your life far easier. Rather than >>> using SplitText, Extract Text, and MergeContent >>> in order to group the text, RouteText will allow you to supply a >>> Grouping Regex. So that regex can just pull out the >>> device id, year, month, and day, from each line and group together lines >>> of text that have the same values into >>> a single FlowFile. For instance, if your CSV looked like: >>> >>> # device_id, device_manufacturer, value, year, month, day, hour >>> 1234, Famous Manufacturer, 83, 2015, 11, 13, 12 >>> >>> You 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 the >>> millions 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 ticket >>> for this here [2]. I'm not sure that it will make it into the 0.4.0 >>> release, though. >>> >>> I hope this helps! >>> >>> Thanks >>> -Mark >>> >>> [1] https://issues.apache.org/jira/browse/NIFI-1008 >>> [2] https://issues.apache.org/jira/browse/NIFI-1161 >>> >>> >>> On Nov 13, 2015, at 10:44 AM, Mark Petronic <markpetro...@gmail.com> >>> 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 file >>> 1. Use UpdateAttribute to parse the incoming CSV file name to obtain a >>> device_id and set that as an attribute on the flow file >>> 2. 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 matter >>> 7. Use ExecuteProcessStream to execute a Hive script that will "alter >>> table add partitions...." for any partitions that were newly created on >>> this schedule run >>> >>> Is 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 >>> >>> >>> >> >
