Stages of Batch Processing

The term ‘data import’ actually understates the range of functions importers really have. As already stated, many importers do not only restore data once backed up by exporters or, in other words, take values from CSV files and write them one-on-one into the database. The data undergo a complex staged data processing algorithm. Therefore, we prefer calling them ‘batch processors’ instead of importers. The stages of the import process are as follows.

Stage 1: File Upload

Users with permission waeup.manageDataCenter are allowed to access the data center and also to use the upload page. On this page they can access an overview of all available batch processors. When clicking on a processor name, required, optional and non-schema fields show up in the modal window. Also a CSV file template, which can be filled and uploaded to avoid header errors, is being provided in this window.

Many importer fields are of type ‘Choice’, which means only definied keywords (tokens) are allowed, see schema fields. An overview of all sources and vocabularies, which feed the choices, can be also accessed from the datacenter upload page and shows up in a modal window. Sources and vocabularies of the base package can be viewed here.

Data center managers can upload any kind of CSV file from their local computer. The uploader does not check the integrity of the content but the validity of its CSV encoding (see check_csv_charset). It also checks the filename extension and allows only a limited number of files in the data center.

DatacenterUploadPage.max_files = 20

If the upload succeeded the uploader sends an email to all import managers (users with role waeup.ImportManager) of the portal that a new file was uploaded.

The uploader changes the filename. An uploaded file foo.csv will be stored as foo_USERNAME.csv where username is the user id of the currently logged in user. Spaces in filename are replaced by underscores. Pending data filenames remain unchanged (see below).

After file upload the data center manager can click the ‘Process data’ button to open the page where files can be selected for import (import step 1). After selecting a file the data center manager can preview the header and the first three records of the uploaded file (import step 2). If the preview fails or the header contains duplicate column titles, an error message is raised. The user cannot proceed but is requested to replace the uploaded file. If the preview succeeds the user is able to proceed to the next step (import step 3) by selecting the appropriate processor and an import mode. In import mode create new objects are added to the database, in update mode existing objects are modified and in remove mode deleted.

Stage 2: File Header Validation

Import step 3 is the stage where the file content is assessed for the first time and checked if the column titles correspond with the fields of the processor chosen. The page shows the header and the first record of the uploaded file. The page allows to change column titles or to ignore entire columns during import. It might have happened that one or more column titles are misspelled or that the person, who created the file, ignored the case-sensitivity of field names. Then the data import manager can easily fix this by selecting the correct title and click the ‘Set headerfields’ button. Setting the column titles is temporary, it does not modify the uploaded file. Consequently, it does not make sense to set new column titles if the file is not imported afterwards.

The page also calls the checkHeaders method of the batch processor which checks for required fields. If a required column title is missing, a warning message is raised and the user can’t proceed to the next step (import step 4).


Data center managers, who are only charged with uploading files but not with the import of files, are requested to proceed up to import step 3 and verify that the data format meets all the import criteria and requirements of the batch processor.

Stage 3: Data Validation and Import

Import step 4 is the actual data import. The import is started by clicking the ‘Perform import’ button. This action requires the waeup.importData permission. If data managers don’t have this permission, they will be redirected to the login page.

Kofa does not validate the data in advance. It tries to import the data row-by-row while reading the CSV file. The reason is that import files very often contain thousands or even tenthousands of records. It is not feasable for data managers to edit import files until they are error-free. Very often such an error is not really a mistake made by the person who compiled the file. Example: The import file contains course results although the student has not yet registered the courses. Then the import of this single record has to wait, i.e. it has to be marked pending, until the student has added the course ticket. Only then it can be edited by the batch processor.

The core import method is:


In contrast to most other methods, doImport is not supposed to be customized, neither in custom packages nor in derived batch processor classes. Therefore, this is the only place where we do import data.

Before this method starts creating or updating persistent data, it prepares two more files in a temporary folder of the filesystem: (1) a file for pending data with file extension .pending and (2) a file for successfully processed data with file extension .finished. Then the method starts iterating over all rows of the CSV file. Each row is treated as follows:

  1. An empty row is skipped.

  2. Empty strings or lists ([]) in the row are replaced by ignore markers.

  3. The BatchProcessor.checkConversion method validates and converts all values in the row. Conversion means the transformation of strings into Python objects. For instance, number expressions have to be transformed into integers, dates into datetime objects, phone number expressions into phone number objects, etc. The converter returns a dictionary with converted values or, if the validation of one of the elements fails, an appropriate warning message. If the conversion fails a pending record is created and stored in the pending data file together with a warning message the converter has raised.

  4. In create mode only:

    The parent object must be found and a child object with same object id must not exist. Otherwise the row is skipped, a corresponding warning message is raised and a record is stored in the pending data file.

    Now doImport tries to add the new object with the data from the conversion dictionary. In some cases this may fail and a DuplicationError is raised. For example, a new payment ticket is created but the same payment for same session has already been made. In this case the object id is unique, no other object with same id exists, but making the ‘same’ payment twice does not make sense. The import is skipped and a record is stored in the pending data file.

  5. In update mode only:

    If the object can’t be found, the row is skipped, a no such entry warning message is raised and a record is stored in the pending data file.

    The BatchProcessor.checkUpdateRequirements method checks additional requirements the object must fulfill before being updated. These requirements are not imposed by the data type but the context of the object. For example, post-graduate students have a different registration workflow. With this method we do forbid certain workflow transitions or states.

    Finally, doImport updates the existing object with the data from the conversion dictionary.

  6. In remove mode only:

    If the object can’t be found, the row is skipped, a no such entry warning message is raised and a record is stored in the pending data file.

    Finally, doImport removes the existing object.

Stage 4: Post-Processing

The data import is finalized by calling distProcessedFiles. This method moves the .pending and .finished files as well as the originally imported file from their temporary to their final location in the storage path of the filesystem from where they can be accessed through the browser user interface.