This is a relatively simple problem with (I'm hoping) a similarly-simple solution.
In my ADF ETLs, any time there's a known and expected yet unrecoverable row-based error, I don't want my full ETL to fail. Instead, I'd rather pipe those rows off to a log, which I can then pick up at the end of the ETL for manual inspection. To do this, I use conditional splits.
Most of the time, there shouldn't be any rows like this. When this is the case, I don't want my blob sink to write a file. However, the current behavior writes a file no matter what -- it's just that the file only contains the table header.
Is there a way to skip writing anything to a blob sink when there are no input rows?
Edit: Somehow I forgot to specify -- I'm specifically referring to a Mapping Data Flow with a blob sink.
You can use Lookup activity(don't check first row only) to get all your table data firstly. Then use If condition to check the count of Lookup activity's output. If its count > 0, execute next activity(or data flow).
Related
NiFi version 1.5
i have a csv file arrives first time like:
datetime,a.DLG,b.DLG,c.DLG
2019/02/04 00:00,86667,98.5,0
2019/02/04 01:00,86567,96.5,0
used listfile -> fetchfile to get the csv file.
next 10 minutes, i get appended csv file:
datetime,a.DLG,b.DLG,c.DLG
2019/02/04 00:00,86667,98.5,0
2019/02/04 01:00,86567,96.5,0
2019/02/04 02:00,86787,99.5,0
2019/02/04 03:00,86117,91.5,0
here, how do we need to get only new records alone (last two records). i do not want to process first two records that is already been processed.
my thought process is, we need to get maximum datetime to store in attribute and use QueryRecord. but i do not know how to get maximum datetime using which processor.
is there any better solution.
This is currently an open issue (NIFI-6047) but there has been a community contribution to address it, so you may see the DetectDuplicateRecord processor in an upcoming release of NiFi.
There may be a workaround to split up the CSV rows and create a compound key using ExtractText, then using DetectDuplicate.
It doesn't seems to be a work that is best solved on Nifi as you need to keep a state of what you have processed. An alternative would be for you to delete what you have already processed. Then you can assume what is in the file is always not processed.
here, how do we need to get only new records alone (last two records).
i do not want to process first two records that is already been
processed.
From my understanding, actual question is 'how to process/ingest csv rows as it is written to the file?'.
Description of 'TailFile' processor from NiFi documentation:
"Tails" a file, or a list of files, ingesting data from the file as it
is written to the file. The file is expected to be textual. Data is
ingested only when a new line is encountered (carriage return or
new-line character or combination)
This solution is appropriate when you don't want to move/delete actual file.
We have a flow where GenerateTableFetch takes inpute from splitJson which gives TableName, ColumnName as argument. At once multiple tables are passed as input to GenerateTableFetch and next ExecuteSql executes the query.
Now i want to trigger a new process when all the files for a table has been processed by the below processor (At the end there is PutFile).
How to find that all the files created for a Table has been processed?
You may need NIFI-5601 to accomplish this, there is a patch currently under review at the time of this writing, I hope to get it into NiFi 1.9.0.
EDIT: Adding potential workarounds in the meantime
If you can use ListDatabaseTables instead of getting your table names from a JSON file, then you can set Include Count to true. Then you will get attributes for the table name and the count of its rows. Then you can divide the count by the value of the Partition Size in GTF and that will give you the number of fetches (let's call it X). Then add an attribute via UpdateAttribute called "parent" or something, and set it to ${UUID()}. Keep these attributes in the flow files going into GTF and ExecuteScript, then you can use Wait/Notify to wait until X flow files are received (setting Target Signal Count to ${X}) and using ${parent} as the Release Signal Identifier.
If you can't use ListDatabaseTables, then you may be able to have ExecuteSQLRecord after your SplitJSON, you can execute something like SELECT COUNT(*) FROM ${table.name}. If using ExecuteSQL, you may need a ConvertAvroToJSON, if using ExecuteSQLRecord use a JSONRecordSetWriter. Then you can extract the count from the flow file contents using EvaluateJsonPath.
Once you have the table name and the row count in attributes, you can continue with the flow I outlined above (i.e. determine the number of flow files that GTF will generate, etc.).
My use case is like this. I have some X tables to be pulled from MySQL. I am splitting them using SplitText to put each table in a individual flow file and pull using GenerateTableFetch and ExecuteSQL.
And I want to be notified or put some other action when import is done for all the tables. At SplitText text processor I have routed original relationship to Wait on ${filename} with target count ${fragment.count}. This will track how many tables are done.
But now I am not able to figure out how to know when a particular table is done. GenerateTableFetch forks flow file into multiple based on Partition Size. But it does not write attributes like fragment.count which I can use to wait on for each table.
Is there a way I can achieve this? Or maybe is there a way to know at the end of the entire flow if all flow files in the flow have been processed and nothing is in queue or being processed?
If you have a standalone instance of NiFi (or are not distributing the flow files among a cluster to ExecuteSQL nodes), then you could use QueryDatabaseTable instead, it (by default) will only issue all flow files when the entire result set is processed. If you have all the rows go into a single flow file, then the fact that the flow file has been transferred downstream is an indication that the fetch is complete.
I have written NIFI-5601 to cover the improvement of adding fragment.* attributes to flow files generated by GTF.
Till NiFi add's support for this, I managed to make it work using MergeContent. Use table_name as Correlation attribute name and then use merged relation to Wait processor using ${merge.count} as target. Refer screenshots if someone is looking to do the same.
I am getting files from remote server using Nifi: my files are as follow:
timestamp (ms), nodeID,value
12345,x,12.4
12346,x,12.7
12348,x,13.4
12356,x,13,6
12355,y,12.0
I am now just get and fetch and split lines and send them to Kafka, but before hand, I need to apply a checksum approach on my records and aggregate them based on time stamp, what I need to do to add an additional column to my content and count the records based on aggregated time stamps, for example aggregation based on each 10 milliseconds and nodeID..
timestamp (ms), nodeID,value, counts
12345,x,12.4,3
12346,x,12.7,3
12348,x,13.4,3
12356,x,13,6,1
12355,y,12.0,1
How to do above process in NiFi. I am totally new to Nifi but need to add above functinality to my Nifi process. I am currently using below nifi process
This may not answer your question directly, but you should consider refactoring your flow to use the "record" processors. It would greatly simplify things and would probably get you closer to being able to do the aggregation.
The idea is to not split up the records, and instead process them in place. Given your current flow, the 4 processors after FetchSFTP would like change to a single ConvertRecord processor that converts CSV to JSON. You would first need to defined a simple Avro schema for your data.
Once you have the record processing setup, you might be able to use PartitionRecord to partition the records by the node id, and then from there the missing piece would be how to count by the timestamps.
Some additional resources...
https://blogs.apache.org/nifi/entry/record-oriented-data-with-nifi
https://bryanbende.com/development/2017/06/20/apache-nifi-records-and-schema-registries
https://www.slideshare.net/BryanBende/apache-nifi-record-processing
Imagine you have a big file stored in hdtf which contains structured data. Now the goal is to process only a portion of data in the file like all the lines in the file where second column value is between so and so. Is it possible to launch the MR job such that hdfs only stream the relevant portion of the file versus streaming everything to the mappers.
The reason is that I want to expedite the job speed by only working on the portion that I need. Probably one approach is to run a MR job to get create a new file but I am wondering if one can avoid that?
Please note that the goal is to keep the data in HDFS and I do not want to read and write from database.
HDFS stores files as a bunch of bytes in blocks, and there is no indexing, and therefore no way to only read in a portion of your file (at least at the time of this writing). Furthermore, any given mapper may get the first block of the file or the 400th, and you don't get control over that.
That said, the whole point of MapReduce is to distribute the load over many machines. In our cluster, we run up to 28 mappers at a time (7 per node on 4 nodes), so if my input file is 1TB, each map slot may only end up reading 3% of the total file, or about 30GB. You just perform the filter that you want in the mapper, and only process the rows you are interested in.
If you really need filtered access, you might want to look at storing your data in HBase. It can act as a native source for MapReduce jobs, provides filtered reads, and stores its data on HDFS, so you are still in the distributed world.
One answer is looking at the way that hive solves this problem. The data is in "tables" which are really just meta data about files on disk. Hive allows you to set columns on which a table is partitioned. This creates a separate folder for each partition so if you were partitioning a file by date you would have:
/mytable/2011-12-01
/mytable/2011-12-02
Inside of the date directory would be you actual files. So if you then ran a query like:
SELECT * FROM mytable WHERE dt ='2011-12-01'
Only files in /mytable/2011-12-01 would be fed into the job.
Tho bottom line is that if you want functionality like this you either want to move to a higher level language (hive/pig) or you need to roll your own solutions.
Big part of the processing cost - is data parsing to produce Key-Values to the Mapper. We create there (usually) one java object per value + some container. It is costly both in terms of CPU and garbage collector pressure
I would suggest the solution "in the middle". You can write input format which will read the input stream and skip non-relevant data in the early stage (for example by looking into few first bytes of the string). As a result you will read all data, but actually parse and pass to the Mapper - only portion of it.
Another approach I would consider - is to use RCFile format (or other columnar format), and take care that relevant and non relevant data will sit in the different columns.
If the files that you want to process have some unique attribute about their filename (like extension or partial filename match), you can also use the setInputPathFilter method of FileInputFormat to ignore all but the ones you want for your MR job. Hadoop by default ignores all ".xxx" and _xxx" files/dirs, but you can extend with setInputPathFilter.
As others have noted above, you will likely get sub-optimal performance out of your cluster doing something like this which breaks the "one block per mapper" paradigm, but sometimes this is acceptable. Can sometimes take more to "do it right", esp if you're dealing with a small amount of data & the time to re-architect and/or re-dump into HBase would eclipse the extra time required to run your job sub-optimally.