The name analyzer is the actual work horse of the tokenizer. It
is instantiated on a thread-base and provides all functions for
analysing names and queries.
Add a jsonb column to the placex and location_property_osmline tables
which can be used by the installed tokenizer as required. No other
part of the software will use or otherwise rely on this column.
Indexing is now split into three parts: first a preparation step
that collects the necessary information from the database and
returns it to Python. In a second step the data is transformed
within Python as necessary and then returned to the database
through the usual UPDATE which now not only sets the indexed_status
but also other fields. The third step comprises the address
computation which is still done inside the update trigger in
the database.
The second processing step doesn't do anything useful yet.
Creating and populating the word table is now the responsibility
of the tokenizer.
The get_maxwordfreq() function has been replaced with a
simple template parameter to the SQL during function installation.
The number is taken from the parameter list in the database to
ensure that it is not changed after installation.
Adds a migration that initialises a legacy tokenizer for
an existing database. The migration is not active yet as
it will need completion when more functionality is added
to the legacy tokenizer.
This adds the boilerplate for selecting configurable tokenizers.
A tokenizer can be chosen at import time and will then install
itself such that it is fixed for the given database import even
when the software itself is updated.
The legacy tokenizer implements Nominatim's traditional algorithms.
These tables have never been actively maintained and the code is
completely untested. With the upcomming changes, it is unlikely
that the code remains usable.
This removes the aux tables and all code that references them.
Indexing scans the placex table sequentially during indexing
on the initial import. That is okay because we know that all
rows need to be processed anywhere. When continuing the import,
however, a large part might already be indexed, so that the
process spends a lot of time going through rows that are no
longer of interest. Create a supporting index for all unindexed
rows to speed up the scan. This is the same index as used later
for updates.
