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 <[email protected]> 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 <[email protected]> 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 <[email protected]> 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 >
