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TEP 2 - Tango database serverless

TEP: 2
Title: Tango database serverless
Version: 1.0.0
Last-Modified: 17-Oct-2012
Author: Tiago Coutinho <>
Status: Active
Type: Standards Track
Content-Type: text/x-rst
Created: 17-Oct-2012
Post-History: 17-Oct-2012


This TEP aims to define a python DataBaseds which doesn’t need a database server behind. It would make tango easier to try out by anyone and it could greatly simplify tango installation on small environments (like small, independent laboratories).


I was given a openSUSE laptop so that I could do the presentation for the tango meeting held in FRMII on October 2012. Since I planned to do a demonstration as part of the presentation I installed all mysql libraries, omniorb, tango and pytango on this laptop.

During the flight to Munich I realized tango was not working because of a strange mysql server configuration done by the openSUSE distribution. I am not a mysql expert and I couldn’t google for a solution. Also it made me angry to have to install all the mysql crap (libmysqlclient, mysqld, mysql-administrator, bla, bla) just to have a demo running.

At the time of writting the first version of this TEP I still didn’t solve the problem! Shame on me!

Also at the same tango meetting during the tango archiving discussions I heard fake whispers or changing the tango archiving from MySQL/Oracle to NoSQL.

I started thinking if it could be possible to have an alternative implementation of DataBaseds without the need for a mysql server.


  • no dependencies on external packages
  • no need for a separate database server process (at least, by default)
  • no need to execute post install scripts to fill database

Step 1 - Gather database information

It turns out that python has a Database API specification (PEP 249). Python distribution comes natively (>= 2.6) with not one but several persistency options (Data Persistence):

module Native Platforms API Database Description
Native python 2.x
pickle Yes all dump/load file python serialization/marchalling module
shelve Yes all dict file high level persistent, dictionary-like object
marshal Yes all dump/load file Internal Python object serialization
anydbm Yes all dict file Generic access to DBM-style databases. Wrapper for dbhash, gdbm, dbm or dumbdbm
dbm Yes all dict file Simple “database” interface
gdbm Yes unix dict file GNU’s reinterpretation of dbm
dbhash Yes unix? dict file DBM-style interface to the BSD database library (needs bsddb). Removed in python 3
bsddb Yes unix? dict file Interface to Berkeley DB library. Removed in python 3
dumbdbm Yes all dict file Portable DBM implementation
sqlite3 Yes all DBAPI2 file, memory DB-API 2.0 interface for SQLite databases
Native Python 3.x
pickle Yes all dump/load file python serialization/marchalling module
shelve Yes all dict file high level persistent, dictionary-like object
marshal Yes all dump/load file Internal Python object serialization
dbm Yes all dict file Interfaces to Unix “databases”. Wrapper for dbm.gnu, dbm.ndbm, dbm.dumb
dbm.gnu Yes unix dict file GNU’s reinterpretation of dbm
dbm.ndbm Yes unix dict file Interface based on ndbm
dbm.dumb Yes all dict file Portable DBM implementation
sqlite3 Yes all DBAPI2 file, memory DB-API 2.0 interface for SQLite databases

third-party DBAPI2

third-party NOSQL

(these may or not have python DBAPI2 interface)

third-party database abstraction layer

  • SQLAlchemy - sqlalchemy - Python SQL toolkit and Object Relational Mapper

Step 2 - Which module to use?

herrrr... wrong question!

The first decision I thought it should made is which python module better suites the needs of this TEP. Then I realized I would fall into the same trap as the C++ DataBaseds: hard link the server to a specific database implementation (in their case MySQL).

I took a closer look at the tables above and I noticed that python persistent modules come in two flavors: dict and DBAPI2. So naturally the decision I thought it had to be made was: which flavor to use?

But then I realized both flavors could be used if we properly design the python DataBaseds.

Step 3 - Architecture

If you step back for a moment and look at the big picture you will see that what we need is really just a mapping between the Tango DataBase set of attributes and commands (I will call this Tango Device DataBase API) and the python database API oriented to tango (I will call this TDB interface).

The TDB interface should be represented by the ITangoDB. Concrete databases should implement this interface (example, DBAPI2 interface should be represented by a class TangoDBAPI2 implementing ITangoDB).

Connection to a concrete ITangoDB should be done through a factory: TangoDBFactory

The Tango DataBase device should have no logic. Through basic configuration it should be able to ask the TangoDBFactory for a concrete ITangoDB. The code of every command and attribute should be simple forward to the ITangoDB object (a part of some parameter translation and error handling).

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    subgraph tangodbPackage {
        label = "Package tangodb"

        ITangoDB [
            label = "{ITangoDB|+ add_device()=0\l+delete_device()=0\l+export_device()=0\l...}"

        DBAPI2 [
            label = "{TangoDBAPI2}"

        Dict [
            label = "{TangoDBDict}"

        DBSqlite3 [
            label = "{TangoDBSqlite3}"

        mxODBC [
            label = "{TangoDBmxODBC}"

        MySQLdb [
            label = "{TangoDBMySQLdb}"

        Shelve [
            label = "{TangoDBShelve}"

        TangoDBFactory [
            label = "{TangoDBFactory|+get_tango_db(): ITangoDB}"

        DBAPI2 -> ITangoDB
        Dict -> ITangoDB
        DBSqlite3 -> DBAPI2
        mxODBC -> DBAPI2
        MySQLdb -> DBAPI2
        Shelve -> Dict

    DeviceImpl [
        label = "{Tango::DeviceImpl}"

    DataBase [
        label = "{DataBase|+DbAddDevice()\l+DbDeleteDevice()\l+DbExportDevice()\l...}"

    DataBase -> DeviceImpl

Step 4 - The python DataBaseds

If we can make a python device server which has the same set of attributes and commands has the existing C++ DataBase (and of course the same semantic behavior), the tango DS and tango clients will never know the difference (BTW, that’s one of the beauties of tango).

The C++ DataBase consists of around 80 commands and 1 mandatory attribute (the others are used for profiling) so making a python Tango DataBase device from scratch is out of the question.

Fortunately, C++ DataBase is one of the few device servers that were developed since the beginning with pogo and were successfully adapted to pogo 8. This means there is a precious DataBase.xmi available which can be loaded to pogo and saved as a python version. The result of doing this can be found here here (this file was generated with a beta version of the pogo 8.1 python code generator so it may contain errors).

Step 5 - Default database implementation

The decision to which database implementation should be used should obey the following rules:

  1. should not require an extra database server process
  2. should be a native python module
  3. should implement python DBAPI2

It came to my attention the sqlite3 module would be perfect as a default database implementation. This module comes with python since version 2.5 and is available in all platforms. It implements the DBAPI2 interface and can store persistently in a common OS file or even in memory.

There are many free scripts on the web to translate a mysql database to sqlite3 so one can use an existing mysql tango database and directly use it with the python DataBaseds with sqlite3 implementation.


The development is being done in PyTango SVN trunk in the PyTango.databaseds module.

You can checkout with:

$ svn co PyTango-trunk


A serverless, file based, database has some disadvantages when compared to the mysql solution:

  • Not possible to distribute load between Tango DataBase DS and database server (example: run the Tango DS in one machine and the database server in another)
  • Not possible to have two Tango DataBase DS pointing to the same database
  • Harder to upgrade to newer version of sql tables (specially if using dict based database)

Bare in mind the purpose of this TED is to simplify the process of trying tango and to ease installation and configuration on small environments (like small, independent laboratories).