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Added by Matt Casters, last edited by Jens Bleuel on May 26, 2010
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Description
This step provides the ability to read data from a delimited file. It has fewer overall options than the general Text File Input step, but it has a few key features over it:
- NIO -- Native system calls for reading the file means faster performance, but it is limited to only local files currently. No VFS support.
- Parallel running -- If you configure this step to run in multiple copies or in clustered mode, and you enable parallel running, each copy will read a separate block of a single file allowing you to distribute the file reading to several threads or even several slave nodes in a clustered transformation.
- Lazy conversion -- If you will be reading many fields from the file and many of those fields will not be manipulate, but merely passed through the transformation to land in some other text file or a database, lazy conversion can prevent Kettle from performing unnecessary work on those fields such as converting them into objects such as strings, dates, or numbers.
Options
The table below describes the options available for the CSV Input step:
| Option | Description |
|---|---|
| Step name | Name of the step.
|
| Filename or The filename field (data from previous steps) |
Specify the name of the CSV file to read from. or Select the fieldname that will contain the filename(s) to read from. If this step receives data from a previous step, this option is enabled as well as the option to include the filename in the output. |
| Delimiter | Specify the file delimiter character used in the target file. Special characters (e.g. CHAR HEX01) can be set with the format $[value], e.g. $[01] or $[6F,FF,00,1F]. |
| Enclosure | Specify the enclosure character used in the target file. Special characters (e.g. CHAR HEX01) can be set with the format $[value], e.g. $[01] or $[6F,FF,00,1F]. |
| NIO buffer size | This is the size of the read buffer. It represents the amount of bytes that is read in one time from disk. |
| Lazy conversion | The lazy conversion algorithm will try to avoid unnecessary data type conversions and can result in a significant performance improvements if this is possible. The typical example that comes to mind is reading from a text file and writing back to a text file. |
| Header row present? | Enable this option if the target file contains a header row containing column names. |
| Add filename to result |
Adds the CSV filename(s) read to the result of this transformation. A unique list is being kept in memory that can be used in the next job entry in a job, for example in another transformation. |
| The row number field name (optional) |
The name of the Integer field that will contain the row number in the output of this step. |
| Running in parallel? |
Check this box if you will have multiple instances of this step running (step copies) and if you want each instance to read a separate part of the CSV file(s). When reading multiple files, the total size of all files is taken into consideration to split the workload. In that specific case, make sure that ALL step copies receive all files that need to be read, otherwise, the parallel algorithm will not work correctly (for obvious reasons).
|
| File Encoding | Specify the encoding of the file being read. |
| Fields Table | This table contains an ordered list of fields to be read from the target file. |
| Preview button | Click to preview the data coming from the target file. |
| Get Fields button | Click to return a list of fields from the target file based on the current settings (i.e. Delimiter, Enclosure, etc.). All fields identified will be added to the Fields Table. |
The number format when importing from a CSV file in Kettle is english-based, meaning that in order to parse a number (12.345.678,99) the format must still be '#,##0.00' with the decimal and group characters set to '.' and ',' to match the European format. Number formatting is done as in the Java Number Classes.
If you are using a CSV file as an input to a stream lookup, you must uncheck the box labeled "Lazy Conversion?" in the CSV File step. Otherwise, your stream lookups will fail with messages like this:Unexpected error :
2008/12/05 16:25:28 - Check Dept ID.0 - ERROR (version 3.1.0, build 826 from 2008/09/30 11:32:36) : java.lang.RuntimeException: Error serializing row to byte array
It may be that you only need to uncheck the Lazy Conversion box on the CSV file that will be the source for the lookup step; however, it's easy enough to uncheck it on all of the CSV files, unless you run into performance issues.