Christian Theune wrote:
<snip />
We imagine we need two kinds of components to make this work:

1. A query processor that could look like:

class IQueryProcessor(Interface):

    def query(...):
        """Returns a list of matching objects. The parameters are
           specific to the query processor in use.
        """

Alternatively, as the signature of the only method isn't specified
anyway, we could make each query processor define its own interface
instead.

2. An object collection that serves two purposes:

a) maintain indexes

b) provide a low-level query API that is rich enough to let different
query processors e.g. for SQL, xpath, ... work against them.

This is the one that needs most work to get the separation of concerns
right. One split we came up with are the responsibilities to define:

- which objects to index
- how to store the indexes
- how to derive the structural relations between objects

Those could be separated into individual components and make the object
collection a component that joins those together.

On the definition of indexes: we're not sure whether a generic set of
indexes will be sufficient (e.g. the three indexes from XISS - class
index, attribute index, structural index) or do those need to be
exchanged?
For our ad-hoc querying we certainly don't want to have to set up
specialised indexes to make things work, but maybe optional indexes
could be used when possible -- just like RDBMS.


Make sure you take a look at SQLAlchemy's implementation of this, sqlalchemy.orm.query.

RDBMS do not get fast querying for free... They just revert to a complete record scan when they do not have an index - analogous to the find tab in the ZMI. As anyone who has ever queried such a database can attest, it ain't quick. (RDBMSs tend to create implicit indexes on primary and foreign keys also.)

Laurence

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