You are assuming that the problem is the switch to the bitmap scan. That is very unlikely.
The likely problem is that your data had to be read from disk, either because it has grown too large to fit in cache (in which case more RAM would help), or the cache was cold when you happened to run the test.
The switch to bitmap scan is likely a response to the same thing that caused the problem, the growth in data.
You could cluster the table on the index t_feature_ind, which would make it more efficient to pull out all of the data with any given feature.
Questionable use case
...each CONTENT entry consists of one random word and a text string that is the same for all rows.
A text string that is the same for all rows is just dead freight. Remove it and concatenate it in a view if you need to show it.
Obviously, you are aware of that:
Granted, it is not realistic ... But since I can't control the text ...
Upgrade your Postgres version
Running PostgreSQL 9.3.4
While still on Postgres 9.3, you should at least upgrade to the latest point release (currently 9.3.9). The official recommendation of the project:
We always recommend that all users run the latest available minor
release for whatever major version is in use.
Better yet, upgrade to 9.4 which has received major improvements for GIN indexes.
Major problem 1: Cost estimates
The cost of some textsearch functions has been seriously underestimated up to and including version 9.4. That cost is raised by factor 100 in the upcoming version 9.5 like @jjanes describes in his recent answer:
Here are the respective thread where this was discussed and the commit message by Tom Lane.
As you can see in the commit message, to_tsvector()
is among those functions. You can apply the change immediately (as superuser):
ALTER FUNCTION to_tsvector (regconfig, text) COST 100;
which should make it much more likely that your functional index is used.
Major problem 2: KNN
The core problem is that Postgres has to calculate a rank with ts_rank()
for 260k rows (rows=261011
) before it can order by and pick the top 5. This is going to be expensive, even after you have fixed other problems as discussed. It's a K-nearest-neighbour (KNN) problem by nature and there are solutions for related cases. But I cannot think of a general solution for your case, since the rank calculation itself depends on user input. I would try to eliminate the bulk of low ranking matches early so that the full calculation only has to be done for few good candidates.
One way I can think of is to combine your fulltext search with trigram similarity search - which offers a working implementation for the KNN problem. This way you can pre-select the "best" matches with LIKE
predicate as candidates (in a subquery with LIMIT 50
for example) and then pick the 5 top-ranking rows according to your rank-calculation in the main query.
Or apply both predicates in the same query and pick the closest matches according to trigram similarity (which would produce different results) like in this related answer:
I did some more research and you are not the first to run into this problem. Related posts on pgsql-general:
Work is ongoing to eventually implement a tsvector <-> tsquery
operator.
Oleg Bartunov and Alexander Korotkov even presented a working prototype (using ><
as operator instead of <->
back then) but it's very complex to integrate in Postgres, the whole infrastructure for GIN indexes has to be reworked (most of which is done by now).
Major problem 3: weights and index
And I identified one more factor adding to the slowness of the query. Per documentation:
GIN indexes are not lossy for standard queries, but their performance
depends logarithmically on the number of unique words. (However, GIN
indexes store only the words (lexemes) of tsvector
values, and not
their weight labels. Thus a table row recheck is needed when using a
query that involves weights.)
Bold emphasis mine. As soon as weight are involved, each row has to be fetched from the heap (not just a cheap visibility check) and long values have to be de-toasted, which adds to the cost. But there seems to be a solution for that:
Index definition
Looking at your index again, it doesn't seem to make sense to begin with. You assign a weight to a single column, which is meaningless as long as you don't concatenate other columns with a different weight.
COALESCE()
also makes no sense as long as you don't actually concatenate more columns.
Simplify your index:
CREATE INDEX "File_contentIndex" ON "File" USING gin
(to_tsvector('english', "CONTENT");
And your query:
SELECT "ITEMID", ts_rank(to_tsvector('english', "CONTENT")
, plainto_tsquery('english', 'searchTerm')) AS rank
FROM "File"
WHERE to_tsvector('english', "CONTENT")
@@ plainto_tsquery('english', 'searchTerm')
ORDER BY rank DESC
LIMIT 5;
Still expensive for a search term that matches every row, but probably much less.
Asides
All of these issues combined, the insane cost of 520 seconds for your second query is beginning to make sense. But there still may be more problems. Did you configure your server?
All the usual advice for performance optimization applies.
It makes your life easier if you don't work with double-quotes CaMeL-case identifiers:
Best Answer
If
hash()
is anIMMUTABLE
function (which should be the case for a function called "hash"!) you can omit storing the functionally dependentattribute_one
in the table altogether and add an expression index to support queries on the expressionhash(customer_one)
:Expression index:
This is exactly as big (identical) as the index supporting your original
UNIQUE
constraint on the redundant columnattribute_one
.Query:
Testing with
EXPLAIN
you'll see index or bitmap index scans like:About the same performance as with the redundant table column or faster since the table is smaller, yet - which helps overall performance in various ways.
Moving the
enum
to first position saves a few bytes of alignment padding per row as explained in my previous answer:Why does the function have to be
IMMUTABLE
? See: