I have some input files with different column names. Can I create a common Nifi flow which processes all the types of files? Each files will be having different types of columns and different types of output table to be loaded. For example, File 1 will be having column A, Column B to be loaded to Table AB, File 2 will be having column C, Column D, Column E to be loaded to Table CDE. Can I achieve this in a single flow or should I create different flows for different types of files? I am new to Nifi, please suggest.
You should be able to do this with a single flow, perhaps with RouteOnContent to look for the header so you know which type of file it is. Each outgoing connection would correspond to a different type of file / output table, so you could have an UpdateAttribute on the other end of each outgoing connection, to set attributes for things like the table name, possibly the record schema (if using record-based processors, which I recommend), etc. Then you can use a funnel to merge the sub-flows, or just connect all the outgoing connections to whatever the next downstream processor is (PutDatabaseTable for example).
If you don't want to split the flow at all, you'd probably need to do the same work of identifying the file type and setting the attributes, but from a single script (using ExecuteScript for example). In any case, the downstream processors should be able to make use of the attributes using NiFi Expression Language such that the same processor can handle the different file types appropriately.
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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 receiving data of formats like csv, xml, json and I want to keep all the files in same hive table.Is it achievable?
Hive expects all the files for one table to use the same delimiter, same compression applied etc. So, you cannot use a Hive table on top of files with multiple formats.
The solution you may want to use is
Create a separate table (json/xml/csv) for each of the file formats
Create a view for the UNION of the 3 tables created above.
This way the consumer of the data has to query only one view/object, if that's what you are looking for.
Yes, you can achieve this through a combination of different external tables.
Because different SerDes with different specifications for how to read columns in the different files will be needed, you will need to create one external table per type of file (and table). The data from each of these external tables can then be combined into a view with UNION, as suggested by Ramesh. The view can could then be used for reading from these, and you could e.g. insert the data into a managed table.
Is it possible to replicate the functionality of link collector in Datastage in Informatica, using its pre-built transformations?
I have 4 different streams in the same mapping. I want a union of all the streams. It may or may not relate to one another. Hence I do not have a common column. I just want to dump values from those 4 streams into a single column in informatica. Is it possible to do so?
The Union transformation allows you to define a number of input groups and input ports (4 groups with single port in your case) and merges the source rows together.
how do we design mapper/reducer if I have to transform a text file line-by-line into another text file.
I wrote a simple map/reduce programs which did a small transformation but the requirement is a bit more elaborate below are the details:
the file is usually structured like this - the first row contains a comma separated list of column names. Second and the rest of the rows specify values against the columns
In some rows the trailing column values might be missing ex: if there are 15 columns then values might be specified only for the first 10 columns.
I have about 5 input files which I need to transform and aggregate into one file. the transformations are specific to each of the 5 input files.
How do I pass contextual information like file name to the mapper/reducer program?
Transformations are specific to columns so how do I remember the columns mentioned in the first row and then correlate and transform values in rows?
Split file into lines, transform (map) each line in parallel, join (reduce) the resulting lines into one file?
You can not rely on the column info in the first row. If your file is larger than a HDFS block, your file will be broken into multiple splits and each split handed to a different mapper. In that case, only the mapper receiving the first split will receive the first row with column info and the rest won't.
I would suggest passing file specific meta data in separate file and distribute it as side data. Your mapper or reducer tasks could read the meta data file.
Through the Hadoop Context object, you can get hold of the name of the file being processed by a mapper. Between all these, I think you have all the context information you are referring to and you can do file specific transformation. Even though the transformation logic is different for different files, the mapper output needs to have the same format.
If you using reducer, you could set the number of reducers to one, to force all output to aggregate to one file.