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Fast OpenERP migration framework

Project description


Reminder This tool is complex and highly experimental and is distributed in the hope that it might be useful, but WITHOUT ANY WARRANTY of FITNESS FOR A PARTICULAR PURPOSE. In particular, if you’re using OpenERP for your company, you should consider purchasing an OpenERP Enterprise contract that will provide you with the best and riskless migration path for your database and custom developments.

This tool has been developped with these initial goals in mind, in this priority order:

  • Merging 2 different OpenERP databases into a single multicompany db
  • Migrating from OpenERP 6.1 to 7.0
  • Migrating data from a legacy business application (Access, Delphi, etc.)
  • Migrating from Dolibarr to OpenERP

The principle of this tool is to export CSV data from an old application (only OpenERP for now), then to process CSV files in order to import them into a freshly installed OpenERP database. This is a completely different strategy than the in-place migration of OpenERP or OpenUpgrade and allows to start over from a clean database, while keeping history. Import and export are done with the PostgreSQL-specific COPY command, and results in extremely fast exports and imports. Combined with a pure in-memory Python csv processing, this tool can often achieve overall migration rates over 2000 lines/sec.


This tool only works with Python 2.7!

With virtualenv

$ virtualenv sandbox
$ sandbox/bin/pip install anybox.migration.openerp
$ sandbox/bin/migrate -h

With Buildout

If you’re using Buildout, you may add this tool in a new part like this:

recipe = zc.recipe.egg
eggs = anybox.migration.openerp

Then, don’t forget to add the migration section in the parts of the [buildout] section. After relaunching bin/buildout, the migrate script will appear in the bin directory of the buildout.

Starting from the version 1.7 of the OpenERP buildout recipe You may also install this tool directly in your [openerp] part, just by adding:

eggs = anybox.migration.openerp
scripts = migrate


This tool offers a single migrate script:

$ migrate -h

You can list the available default mapping files:

$ migrate -l

You should specify the source and target DBs, a selection of the source tables to migrate, and the mapping files to use. The tool then takes care of selecting the dependant tables:

$ migrate -s source_dbname -t target_dbname -r res_partner account_move -p openerp6.1-openerp7.0.yml custom.yml

If you want to inspect the temporary CSV files created, use the --keepcsv option. They will be stored in a temporary directory under the current directory.

This script won’t actually write anything in the target database unless you specify the -w option to commit the transaction at the end.

The most important part of the migration process is the YML mapping file, which describes how to handle data, table by table and column by column. A default mapping file is provided and is being used as a real mapping for a migration consisting in migrating two 6.1 databases into a single 7.0 multicompany database. You can mix the default 6.1 to 7.0 file provided, and augment it with other custom yml files, they will be merged.


This tool was very loosely inspired from:

  • the external_referential OpenERP module
  • the OpenUpgrade project
  • Talend Open Studio

The different internal steps are:

  • Exporting CSV from the old database
  • Transforming CSV to match the target database
  • Detect data existing in the target DB with discriminators
  • Postprocessing CSV files to fix foreign keys
  • Reinjecting into OpenERP
  • Updating possible pre-existing data with incoming data

The processing of CSV files is done using a mapping file written in Yaml. Initial versions of the mapping file have been written with the help of the OpenUpgrade analysis files.

Mapping file

You should keep in mind that this migration tool is only dealing with database tables and columns: the OpenERP fields are unknown to it. Each table, each line, each cell of the source database is handled independently and the mapping file tells what to do with the current cell. This leads to limitations and this tool won’t be able to handle extremely complex migration. But it is powerful enough to allow to simultaneously merge and migrate two 6.1 databases into a 7.0 multicompany database.

For a real-life example, you can have a look at the OpenERP 6.1 to 7.0 mapping file provided in the mappings directory of this tool.

Copying data

The most simple and basic YML statement for a column mapping is the following:

        table2.column2: __copy__

It tells that, if the OpenERP module is installed in the target database, the column1 of the table1 from the source DB should be copied to the column2 of the table2 in the target DB.

The __copy__ instruction can even be omitted and the previous statement is equivalent to this one:


Internally, this statement is actually converted to a Python dict:

        {'table2.column2': '__copy__'}}

And the whole yml file is converted to a large mapping dict whose leafs are statements or functions which are able to process data.

Copying all columns of a table

If your target table has the same structure as the source table, you can avoid specifying one mapping statement for each column and use a wildcard:


It means: copy all the columns of table1 from the source db to table1 in the target db. This kind of mapping is often used as a starting point when source and table structures are similar. You can then add mapping statements for specific columns to override this wildcard.

Copying all columns to a different table

If the source table has only been renamed, you can copy all the columns of the source table1 to the target table2:


Copying everything

If the source and target db have exactly the same structure and you just want to transfer data, you may use a global wildcard (but we have not had the opportunity to try this one for real yet):


It means: copy all tables to the target database without processing. It may seem unuseful compared to a bare dump and restore, but remind that this way you can append data to the target DB, not only replace it. In that case you should take care of existing data, if the table has constraints (see discriminators below)

Splitting one source line to several tables (table splitting)

For a single source line coming from a source table, you can feed data in several target tables. This can be done just by putting several target lines like this:


It means: for each column1 in the table1 of the source DB, create two target lines: one for table2 and one for table3.

During the processing of the current line, other mapping statements can feed the same target lines. Take this example:


In this case, data in the table1 will be directed to table2 and table3. You can then add more lines to handle all the columns of table1

However in the example above, there is a conflict since two source cells are directed to the same target cell (table2.column2). In this scenario, there is no way to predict which one will be used (because the mapping is a Python dict and a dict is not ordered). You should avoid this kind of conflicts.

In case of an OpenERP 6.1 to 7.0 migration, this kind of mapping is actually used to migrate one source res_users line to three different lines: one in res_users + one in res_partner + one in mail_alias. See the default mapping for a real example.

Data moved to another table (table merging)

When input lines must move to a different table, you want the foreign keys pointing to them to be kept so that they point to the new table after migration, you should use the __moved__ statement.

The only current situation in OpenERP is for the res_partner_address data moving to the res_partner table:

base: __moved__

This statement must be accompanied with a __fk__ statement for all the foreign keys pointing to the moved table (See the __fk__ chapter).

Not migrating a column

If you want to get rid of a specific column in a table, use the __forget__ statement:

    table1.column1: __forget__

This statement is useful if you defined a wildcard, to prevent from migrating a specific column.

Transforming data with Python code

Instead of just copying data with the __copy__ statement, you can use any Python code. The Python code should be written in a literal Yaml block and is executed as is, as a function body, so that you have to insert a return statement somewhere.

Example from the mail module:

        mail_message.type: return 'email'

It means the type column of the mail_message table will be filled with 'email' strings, whatever data the source column had.

The eventual signature of the function constructed using the Python code block is

def mapping_function(self, source_row, target_rows):

It means that in the function body you can access the full source_row, which is a dict containing all the keys (column names) and values of the current line being processed. But keep in mind that at this time, you are dealing with one specific cell of this line, and you should return the value that will be inserted in the corresponding cell of the target table. This can be used to aggregate data from two source cells into a target cell:

    table1.firstname: __forget__ return source_row['firstname'] + ' ' + source_row['name']

You can also access the target_rows beeing filled during the processing of the line, so that data coming from a source cell can influence several cells in the target lines, or even different target tables. Here is an example:

base: |
            name = source_row['firstname'] + ' ' + source_row['name']
            target_rows['table1']['display_name'] = name
            target_rows['table2']['display_name'] = name
            return name

Note that in the example above, the Python code spans on several lines, and you should define a Yaml literal block using |. The example above eventually means: append firstname to name coming from the table1, and put it in the display_name cell of the target table1 and table2. The target name cell will contain a copy of the source name cell.

If the target line is not supposed to have the same id as the source line, you can create a new id with the newid() function. This function returns a different value at each call and is responsible of incrementing the id. Here is an example:

        res_partner.notification_email_send: return 'comment' |
            i = newid()
            target_rows['res_users']['partner_id'] = i
            target_rows['res_partner']['id'] = i
            target_rows['res_partner']['name'] = source_row['name']
            target_rows['res_partner']['email'] = source_row['user_email']
            return i

Each res_users line will generate a new res_partner line with a new id, while the res_users id will be the same as the source. (Actually it will not be the same, because an offset is applied to all ids).

Feeding a new column

If a target column should contain data but has no equivalent in the source table, you can use ‘_’ as a substitute to the not existing source column name:

        res_partner.is_company: return False

Merging with existing data

When data is inserted in the target table, you may want to merge it with existing data.

Imagine the target res_users table already contains an admin account, and you don’t want to duplicate this account by migrating data from the source res_users table. In this case you should tell the mapping how to recognize existing data. This is done by replacing the source column name with the __discriminator__ statement, and by providing a list of column names that will be used to recognize existing data:

        - login

Using this statement, you can install a new OpenERP database with its admin account, and merge all existing accounts with data coming from the source table. The login column will be used to match data. The preexisting admin account won’t be duplicated but will be updated with the admin account from the source table.

Another use case in a multicompany scenario is to merge partners existing in the target database, but keep them separate for the two companies:

        - name
        - company_id

Foreign keys without constraints

The first step of the migration is to automatically detect all the foreign keys of the source and target tables. Sometimes, OpenERP defines foreign keys without constraints. This mainly happens with related fields with store=True, which create a column of integers without constraints. If you don’t want to __forget__ such columns, you have to tell the mapping what the target of the foreign key is, like in the real example below:

        account_move.company_id: __fk__ res_company

Here is another example for the crm_lead table, which may contain a field coming from a __moved__ table. Imagine you want the partner_id field of the CRM leads in OpenERP 7.0 to come from the partner_address_id field of the same table in OpenERP 6.1. The new field is a foreign key to res_partner, while the old one was pointing to res_partner_address. You can tell this with the following statement:

    crm_lead.partner_id: __fk__ res_partner_address

However you should also not forget to forget the partner_id field, or you will have a conflict an mix data badly if you used a wildcard for the table:

crm_lead.partner_id: __forget__

Reference fields

Sometimes columns define a dynamic reference id to another table, just like a foreign key, except that the name of the table is actually stored in another column.

id model res_id
1 cr.claim 23
2 cr.claim 35
3 base.action.rule 27

In the example above, since the res_id is not a real foreign key, its value won’t be fixed to correspond to the target database. In that case you should use the __ref__ statement, followed by the name of the column holding the table or model name. This statement assumes the model-to-table transformation of OpenERP is used (replacing ‘.’ with ‘_’):

    mail_message.res_id: __ref__ model

Handle cyclic dependant tables

During the last step, the migrated CSV files are imported one by one. Some tables depend on other tables through foreign key constraints, and such dependencies sometimes happen to be cyclic. In that case, there is no way to import tables because they all depend on another one. One solution is to __forget__ the column, which is rarely desirable because you lose data. To be able to keep such data, you should use the __defer__ statement, so that the column will be updated after all the data is imported:

        res_users.create_uid: __defer__
        res_users.write_uid: __defer__

running SQL requests during migration

In case the wanted migration is too complex to be handled by regular statements, you can run SQL queries on both the source and target database. This should be used in limited cases because the queries will be executed for each source cell for which the mapping defines it, and the migration may be slowed down, unless you limit the queries with manual caching. (See the workflow migration in the mapping).

A simple sql() function is available in the mapping file, and has the following signature:

sql(db, query, args)

- db is the string 'source' or 'target'
- query is the SQL query
- args is the arguments to insert in the query
The query is actually executed with: cursor.execute(query, args)

Here is an example:

        mail_alias.alias_model_id: return sql('target', "select id from ir_model where model='res.users'")[0][0]

Field size limit

When running migration, you may encounter a csv.Error: field larger than field limit. This is due to the csv module limiting the csv field size to 128k by default. The default value has been increased to 20MB. If this is not enough for your migration, you can increase the limit by inserting a direct call to csv.field_size_limit().

For example:

        table2.column2: |
            import csv
            return source_row['column1']

Overall migration process

Migrating need several steps described below. If you need, you can easily write a small script to automate this full process.

Before migration

The different steps before migration are the following. All of them are important for the migration to be successful and should be done on the target db:

  • Create a clean target database without demo data, using the latest migrated code
  • Install the expected modules
  • Rename the target company so that its name exactly match the company in the source database
  • Remove the company of all internal sequences by running the following SQL: update ir_sequence set company_id=NULL;. This will allow to remove duplicate after migration.


The migration consists in running the migrate script by selecting the correct options. If the data in the target database are not the one you expect, you must adapt the options and the mapping file to obtain what you want.

Here is a real example

../bin/migrate -s sourcedb -t targetdb -p openerp6.1-openerp7.0.yml custom.yml
-r res_partner account_move res_users pos_order pos_order_line account_move_line
account_journal sale_order_line stock_inventory_line account_tax
product_supplierinfo wkf_instance wkf_workitem wkf_triggers -w

After migration

The migrate script alone may not be sufficient for your database to be clean and usable. You may have to handle additional corrections. Please test your instance thoroughly! Since version 0.6 you shouldn’t have to manually fix the internal sequences, as they are now handled by the mapping file. You may just clean them up to remove duplicates (from the menu Settings/Technical/Sequences&Identifiers/Sequences). However one of the required remaining fixes consists in dropping some parent_left and parent_right columns. Here is the example with the accounting module:

psql targetdb -c 'alter table account_account drop parent_left;'
psql targetdb -c 'alter table account_account drop parent_right;'

You might also need to force a recalculation of new or changed related fields that are persisted in the database (store=True). Here is an example with the account_report_company module:

psql targetdb -c 'alter table account_invoice drop commercial_partner_id;'

At the end, you should run a final global update of the database. If you’re using the buildout recipe it should look like this:

../bin/start_openerp -u all -d targetdb --stop-after-init

Understanding errors

The most difficult part of using this tool is to understand the errors during the processing, as it requires a deep knowledge of how it internally works. Most errors generally come from an erroneous mapping file. Errors can happen during the processing of the CSV files, but the most difficult ones come from the last import step, because some tables may fail to be imported. In this case, you should carefully look at the logging messages at the end, and try to understand the constraint errors or why tables cannot be imported. You also should use the --keepcsv option, and inspect the intermediate CSV files to understand the problem. By using this option, you will end up with a directory containing five CSV files for each table.

For instance, for the res_partner table you will find these files:

  • res_partner.csv is the original data exported from the source database
  • contains data after the first processing with the mapping file, but wrong foreign keys
  • res_partner.target2.csv contains final data with fixed foreign keys, that will eventually be imported at the end
  • res_partner.update.csv contains data which have been detected as existing in the target database, with wrong foreign keys.
  • res_partner.update2.csv contains the final existing data with fixed foreign keys, that will be used to update the target table after import.

If you’re going into trouble during the import step with foreign key errors, please have a look at this log, as it contains most of the common encountered issues to solve:


Authors and contributors

  • Christophe Combelles
  • Florent Jouatte
  • Guy-Clovis Nzouendjou
  • Stéphane Bidoul

Code repository and bug tracker

See here:

Please don’t hesitate to give us feedback, report bugs or contribute the mapping files on Bitbucket.


0.9 (2013-12-31)

  • cleaned up and added missing tables
  • Many mapping improvements, fixes and cleanup
  • Added mapping for HR migration

0.8 (2013-09-30)

  • restored mapping for users/company assignment
  • improved message migration

0.7 (2013-09-24)

  • Fixed CRM migration
  • Migrate email_template
  • Migrate email_message
  • Support references with __ref__ statements
  • Fixed and improved the mapping
  • Increased the default csv field size limit to 20MB

0.6 (2013-08-24)

  • Migrate ir_sequence without needing post-migration script
  • Fixed workflow instance and workitem migration
  • Major performance improvement (x3) in case of db merging
  • Fixed unwanted merging due to bad offset of foreign key discriminators
  • Break some dependency loops and other mapping improvements

0.5 (2013-08-02)

  • Fixed foreign keys pointing to a __moved__ table with existing data
  • Updated doc

0.4 (2013-07-28)

  • Fixed migration of leads and purchase orders
  • simplified __moved__ statement handling
  • improved workflow migration
  • migrate employees and expenses
  • set suppliers as companies by default
  • how to install in a buildout
  • updated doc

0.3 (2013-07-11)

  • Lots of improvements for the 6.1 to 7.0 migration
  • Fixed a bug during import due to bad quoting
  • Allow m2o to m2m migration without custom code
  • Added mapping for project, crm and auth_ldap modules
  • Fixed move lines
  • Allow to request the source db as well
  • Improved documentation
  • Migration of running workflows

0.2 (2013-07-01)

  • initial release

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