The SplitToAttribute processor for Apache Nifi will allow to split the incoming content (CSV) of a flowfile into separate fields using a defined separator.
The values of the individual fields will be assigned to flowfile attributes. Each attribute is named using the defined field prefix plus the positional number of the field.
A number format can optionally be specified to format the column number. The number format needs to be according to the Java DecimalFormat class.
Example:
A flow file with following content:
Peterson, Jenny, New York, USA
When the field prefix is set to "column_" and the field number format is set to "000" the result will be 4 attributes:
column_000 = Peterson column_001 = Jenny column_002 = New York column_003 = USA
Note that this processor can be used together with the MergeTemplate processor, which merges the flow file attributes with Apache Velocity templates.
The MergeTemplate processor for Apache Nifi will allow to merge the attributes from a flowfile with an Apache Velocity template. The Velocity template contains placeholders (e.g. $column0 - alternatively in brackets: ${column0}).
In the merge process the attributes of the flowfile will be merged with the template and the placeholders are replaced with the attribute values.
See the Apache Velocity website at http://velocity.apache.org for details on the template engine.
A filter (regular _expression_) has to be specified, defining which attributes shall be considered for the template engine.
The original file will be routed to the "original" relation and the result of the merge process will replace the content of the flowfile and is routed to the "merged" relationship.
Example:
A flow file with following attributes:
column0 = Peterson column1 = Jenny column2 = New York column3 = USA
A template file "names.vm" with below format. Placeholders start with a dollar sign and are optionally in curly brackets:
{ "name": "$column0", "first": "$column1", "city": "$column2", "country": "$column3" }
After the attributes are merged with the template, the placeholders in the template are replaced with the values from the flowfile attributes. This is the result:
{ "name": "Peterson", "first": "Jenny", "city": "New York", "country": "USA" }
Can be used for any textual data formats such as CSV, HTML, XML, Json, etc.
The RuleEngine processor allows to process rows of data from CSV files. It runs business rules against the data and then updates the flowfile attributes based on the results of the ruleengine. One can then route the flowfile based on these attributes.
The processor requires to set the ruleengine project zip file and a separator. The project zip file can be created with the Business Rules Maintenance Tool - a web application to construct and orchestrate business logic (business rules). The projects from the web app can be exported and used with the RuleEngine processor. When the ruleengine runs, it splits the incomming row of data (the flowfile content) into individual fields. So the separator defines how the fields are separated from each other.
The advantage of using this processor and a ruleengine is that the business logic can be defined outside of Nifi. And thus if the logic changes, the flow does NOT have to be changed. The ruleengine can be used to define complex business logic. E.g. "Lastname must be XXX and age must be greater than 25 and country must be Germany or France". This would be difficult to model in nifi and would clutter the flow. Managed in the web app the business logic can modified in an agile way and the flow in Nifi stays clean and lean.
Note that there is a test project file: nifi_test2_dev.zip and it can be used with the test data file: allCountries_100.txt. If you use these files, you won't need to install/use the Business Rules Maintenance Tool web application.
The GenerateData processor generates random data from word lists, regular expressions or purely random. The output is in CSV format. This is useful if you want to generate mass data, but mass data which makes sense (from wordlists e.g.). Also there are some nice features for generating dependent date columns.
