postgresql/contrib/fulltextindex/README
Bruce Momjian 8f0ca623ff I finally got the time to put together some stuff for fti for
inclusion in pgsql. I have included a README which should be enough
to start using it, plus a BENCH file that describes some timings
I have done.

Please have a look at it, and if you think everything is OK, I
would like it seen included in the contrib-section of pgsql.

I don't think I will do any more work in this, but maybe it inspires
somebody else to improve on it.

Maarten Boekhold
1998-07-19 18:26:41 +00:00

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An attempt at some sort of Full Text Indexing for PostgreSQL.
The included software is an attempt to add some sort of Full Text Indexing
support to PostgreSQL. I mean by this that we can ask questions like:
Give me all rows that have 'still' and 'nash' in the 'artist' field.
Ofcourse we can write this as:
select * from cds where artist ~* 'stills' and artist ~* 'nash';
But this does not use any indices, and therefore, if your database
gets very large, it will not have very high performance (the above query
requires at least one sequential scan, it probably takes 2 due to the
self-join).
The approach used by this add-on is to define a trigger on the table and
column you want to do this queries on. On every insert in the table, it
takes the value in the specified column, breaks the text in this column
up into pieces, and stores all sub-strings into another table, together
with a reference to the row in the original table that contained this
sub-string (it uses the oid of that row).
By now creating an index over the 'fti-table', we can search for
substrings that occur in the original table. By making a join between
the fti-table and the orig-table, we can get the actual rows we want
(this can also be done by using subselects, and maybe there're other
ways too).
As an example we take the previous query, where we assume we have all
sub-strings in the table 'cds-fti':
select c.*
from cds c, cds-fti f1, cds-fti f2
where f1.string ~ '^stills' and
f2.string ~ '^nash' and
f1.id = c.oid and
f2.id = c.oid ;
We can use the ~ (case-sensitive regular expression) here, because of
the way sub-strings are built: from right to left, ie. house -> 'se' +
'use' + 'ouse' + 'house'. If a ~ search starts with a ^ (match start of
string), btree indices can be used by PostgreSQL.
Now, how do we create the trigger that maintains the fti-table? First: the
fti-table should have the following schema:
create cds-fti ( string varchar(N), id oid );
Don't change the *names* of the columns, the varchar() can in fact also
be of text-type. If you do use varchar, make sure the largest possible
sub-string will fit.
The create the function that contains the trigger::
create function fti() returns opaque as '/path/to/fti.so' language 'C';
And finally define the trigger on the 'cds' table:
create trigger cds-fti-trigger after update or insert or delete on cds
for each row execute procedure fti(cds-fti, artist);
Here, the trigger will be defined on table 'cds', it will create
sub-strings from the field 'artist', and it will place those sub-strings
in the table 'cds-fti'.
Now populate the table 'cds'. This will also populate the table 'cds-fti'.
It's fastest to populate the table *before* you create the indices.
Before you start using the system, you should at least have the following
indices:
create index cds-fti-idx on cds-fti (string, id);
create index cds-oid-idx on cds (oid);
To get the most performance out of this, you should have 'cds-fti'
clustered on disk, ie. all rows with the same sub-strings should be
close to each other. There are 3 ways of doing this:
1. After you have created the indices, execute 'cluster cds-fti-idx on cds-fti'.
2. Do a 'select * into tmp-table from cds-fti order by string' *before*
you create the indices, then 'drop table cds-fti' and
'alter table tmp-table rename to cds-fti'
3. *Before* creating indices, dump the contents of the cds-fti table using
'pg_dump -a -t cds-fti dbase-name', remove the \connect
from the beginning and the \. from the end, and sort it using the
UNIX 'sort' program, and reload the data.
Method 1 is very slow, 2 a lot faster, and for very large tables, 3 is
preferred.