OpenERP has included a bulk import facility for CSV-ish files for a long time. With 7.0, both the interface and internal implementation have been redone, resulting in :meth:`~openerp.osv.orm.BaseModel.load`.
Note
the previous bulk-loading method, :meth:`~openerp.osv.orm.BaseModel.import_data`, remains for backwards compatibility but was re-implemented on top of :meth:`~openerp.osv.orm.BaseModel.load`, while its interface is unchanged its precise behavior has likely been altered for some cases (it shouldn't throw exceptions anymore in many cases where it previously did)
This document attempts to explain the behavior and limitations of :meth:`~openerp.osv.orm.BaseModel.load`.
The input data is a regular row-major matrix of strings (in Python
datatype terms, a list of rows, each row being a list of
str, all rows must be of equal length). Each row must be the same
length as the fields list preceding it in the argslist.
Each field of fields maps to a (potentially relational and nested)
field of the model under import, and the corresponding column of the
data matrix provides a value for the field for each record.
Generally speaking each row of the input yields a record of output, and each cell of a row yields a value for the corresponding field of the row's record. There is currently one exception for this rule:
Because O2M fields contain multiple records "embedded" in the main one, and these sub-records are fully dependent on the main record (are no other references to the sub-records in the system), they have to be spliced into the matrix somehow. This is done by adding lines composed only of o2m record fields below the main record:
.. literalinclude:: 06_misc_import_o2m.txt
the sections in double-lines represent the span of two o2m
fields. During parsing, they are extracted into their own data
matrix for the o2m field they correspond to.
Here are the phases of import. Note that the concept of "phases" is fuzzy as it's currently more of a pipeline, each record moves through the entire pipeline before the next one is processed.
The first phase of the import is the extraction of the current row
(and potentially a section of rows following it if it has One to Many
fields) into a record dictionary. The keys are the fields
originally passed to :meth:`~openerp.osv.orm.BaseModel.load`, and the
values are either the string value at the corresponding cell (for
non-relational fields) or a list of sub-records (for all relational
fields).
This phase also generates the rows indexes for any
:ref:`import-message` produced thereafter.
This second phase takes the record dicts, extracts the :term:`database ID` and :term:`external ID` if present and attempts to convert each field to a type matching what OpenERP expects to write.
- Empty fields (empty strings) are replaced with the
Falsevalue - Non-empty fields are converted through :class:`~openerp.addons.base.ir.ir_fields.ir_fields_converter`
Note
if a field is specified in the import, its default will never be used. If some records need to have a value and others need to use the model's default, either specify that default explicitly or do the import in two phases.
Are returned as-is, without any alteration.
The string value is compared (in a case-insensitive manner) to 0,
false and no as well of any translation thereof loaded in the
database. If the value matches one of these, the field is set to
False.
Otherwise the field is compared to 1, true and yes (and
any translation of these in the database). The field is always set to
True, but if the value does not match one of these a warning will
also be output.
The field is parsed with Python's built-in conversion routines
(int and float respectively), if the conversion fails an error
is generated.
The field is compared to 1. the values of the selection (first part of each selection tuple) and 2. all translations of the selection label found in the database.
If one of these is matched, the corresponding value is set on the field.
Otherwise an error is generated.
The same process applies to both list-type and function-type selection fields.
If the specified field is the relational field itself (m2o), the
value is used in a name_search. The first record returned by
name_search is used as the field's value.
If name_search finds no value, an error is generated. If
name_search finds multiple value, a warning is generated to warn
the user of name_search collisions.
If the specified field is a :term:`external ID` (m2o/id), the
corresponding record it looked up in the database and used as the
field's value. If no record is found matching the provided external
ID, an error is generated.
If the specified field is a :term:`database ID` (m2o/.id), the
process is the same as for external ids (on database identifiers
instead of external ones).
The field's value is interpreted as a comma-separated list of names, external ids or database ids. For each one, the process previously used for the many to one field is applied.
For each o2m record extracted, if the record has a name,
:term:`external ID` or :term:`database ID` the :term:`database ID` is
looked up and checked through the same process as for m2o fields.
If a :term:`database ID` was found, a LINK_TO command is emmitted, followed by an UPDATE with the non-db values for the relational field.
Otherwise a CREATE command is emmitted.
The value's format is checked against :data:`~openerp.tools.misc.DEFAULT_SERVER_DATE_FORMAT`, an error is generated if it does not match the specified format.
The value's format is checked against :data:`~openerp.tools.misc.DEFAULT_SERVER_DATETIME_FORMAT`, an error is generated if it does not match.
The value is then interpreted as a datetime in the user's timezone. The timezone is specified thus:
- If the import
contextcontains atzkey with a valid timezone name, this is the timezone of the datetime. - Otherwise if the user performing the import has a
tzattribute set to a valid timezone name, this is the timezone of the datetime. - Otherwise interpret the datetime as being in the
UTCtimezone.
If the conversion was successful, the converted record is then saved
to the database via (ir.model.data)._update.
The import process will only catch 2 types of exceptions to convert
them to error messages: ValueError during the conversion process,
and sub-exceptions of psycopg2.Error during the create/write
process.
The import process uses savepoint to:
- protect the overall transaction from the failure of each
_updatecall, if an_updatecall fails the savepoint is rolled back and the import process keeps going in order to obtain as many error messages as possible during each run. - protect the import as a whole, a savepoint is created before starting and if any error is generated that savepoint is rolled back. The rest of the transaction (anything not within the import process) will be left untouched.
A message is a dictionary with 5 mandatory keys and one optional key:
type- the type of message, either
warningorerror. Anyerrormessage indicates the import failed and was rolled back. message- the message's actual text, which should be translated and can be shown to the user directly
rows- a dict with 2 keys
fromandto, indicates the range of rows indatawhich generated the message record- a single integer, for warnings the index of the record which
generated the message (can be obtained from a non-false
idsresult) field- the name of the (logical) OpenERP field for which the error or warning was generated
moreinfo(optional)A string, a list or a dict, leading to more information about the warning.
- If
moreinfois a string, it is a supplementary warnings message which should be hidden by default - If
moreinfois a list, it provides a number of possible or alternative values for the string - If
moreinfois a dict, it is an OpenERP action descriptor which can be executed to get more information about the issues with the field. If present, thehelpkey serves as a label for the action (e.g. the text of the link).
- If