This is why we need an expression like Input.getString() to obtain the actual value. With compatibility mode enabled, the step behaves like it did in Kettle 2.5 (and earlier versions): fields from the input stream are considered to be objects, and a special getter method is required to obtain their value. Compatibility mode affects the way the fields of the stream are bound to the javascript step. Like I mentioned, the original transformation uses the Javascript step in compatibility mode. On my laptop, using Pentaho Data Integration 3.2, this transformation takes 21.6 seconds to complete, and the Javascript step processes the rows at a rate of 46210.7 rows/second. I am not entirely sure the dummy ste has any effect on the performance of the javascript step, but I figured it would be a good idea to ensure the output of the script is actually copied to an outgoing stream.
#Pentaho data integration variables code
The rows are processed by the Modified Javascript Value step, using the original code and compatibility mode like described in Vincent's original post.
The transformation uses a Generate Rows step to generate 1 million rows having a single String type field with the default value vIncEnt. I used the following transformation to test this script: For some reason, the original example uses the javascript step in compatibility mode, necessitating expressions such as Input.getString() to obtain the value from the field. In the script, Input is the string field in the incoming stream that is to be modified, and cc is added to the output stream, containing the modified value.
#Pentaho data integration variables free
Please feel free and leave a comment if you can provide more detailed information with regard to this matter.)
Both tools use an embedded Rhino engine as javascript runtime, but I can imagine that there are slight differences with regard to binding the input and output fields and the support for built-in functions. While I don't have much experience with that tool, I think the proper step to use in that case is the tRhino step. (The original post explains that one should be able to execute the code with minimal modification in Talend. Var cc = upper(c) + lower(Input.getString().slice(1)) If (parseInt(Input.getString().length)=1) First letter in uppercase, others in lowercase He uses it to execute the following piece of code: Vincent illustrates his solution using Pentaho Data Integration's "Modified Javascript Value" step. For example: vIncEnt should become Vincent.
The problem described by Vincent is simple enough: for each input string, return the string in lower case, except for the initial character, which should be in upper case. Other improvements have to do with the way the incoming and outgoing record streams are bound to the javascript step in Kettle. Some of these improvements are generic: because they apply to the use of the javascript language, they are likely to improve performance in both Talend as well as Kettle. I will present a few modifications that considerably improve performance of the Javascript execution. In this post, I am looking at a few details of Vincent's approach to using Javascript in his transformation. I just read a post from Vincent Teyssier on cleaning strings using the javascript capabilities of Pentaho Data Integration (also known as Kettle) and Talend.
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