Background
I'm implementing full-text search over a body of email messages stored in SQLite, making use of its fantastic built-in FTS4 engine. I'm getting some rather poor query performance, although not exactly where I would expect. Let's take a look.
Representative schema
I'll give some simplified examples of the code in question, with links to the full code where applicable.
We've got a MessageTable
that stores the data about an email message (full version spread out over several files here, here, and here):
CREATE TABLE MessageTable (
id INTEGER PRIMARY KEY,
internaldate_time_t INTEGER
);
CREATE INDEX MessageTableInternalDateTimeTIndex
ON MessageTable(internaldate_time_t);
The searchable text is added to an FTS4 table named MessageSearchTable
(full version here):
CREATE VIRTUAL TABLE MessageSearchTable USING fts4(
id INTEGER PRIMARY KEY,
body
);
The id
in the search table acts as a foreign key to the message table.
I'll leave it as an exercise for the reader to insert data into these tables (I certainly can't give out my private email). I have just under 26k records in each table.
Problem query
When we retrieve search results, we need them to be ordered descending by internaldate_time_t
so we can pluck out only the most recent few results. Here's an example search query (full version here):
SELECT id
FROM MessageSearchTable
JOIN MessageTable USING (id)
WHERE MessageSearchTable MATCH 'a'
ORDER BY internaldate_time_t DESC
LIMIT 10 OFFSET 0
On my machine, with my email, that runs in about 150 milliseconds, as measured via:
time sqlite3 test.db <<<"..." > /dev/null
150 milliseconds is no beast of a query, but for a simple FTS lookup and indexed order, it's sluggish. If I omit the ORDER BY
, it completes in 10 milliseconds, for example. Also keep in mind that the actual query has one more sub-select, so there's a little more work going on in general: the full version of the query runs in about 600 milliseconds, which is into beast territory, and omitting the ORDER BY
in that case shaves 500 milliseconds off the time.
If I turn on stats inside sqlite3
and run the query, I notice the line:
Sort Operations: 1
If my interpretation of the docs about those stats is correct, it looks like the query is completely skipping using the MessageTableInternalDateTimeTIndex
. The full version of the query also has the line:
Fullscan Steps: 25824
Sounds like it's walking the table somewhere, but let's ignore that for now.
What I've discovered
So let's work on optimizing that a little bit. I can rearrange the query into a sub-select and force SQLite to use our index with the INDEXED BY
extension:
SELECT id
FROM MessageTable
INDEXED BY MessageTableInternalDateTimeTIndex
WHERE id IN (
SELECT id
FROM MessageSearchTable
WHERE MessageSearchTable MATCH 'a'
)
ORDER BY internaldate_time_t DESC
LIMIT 10 OFFSET 0
Lo and behold, the running time has dropped to around 100 milliseconds (300 milliseconds in the full version of the query, a 50% reduction in running time), and there are no sort operations reported. Note that with just reorganizing the query like this but not forcing the index with INDEXED BY
, there's still a sort operation (though we've still shaved off a few milliseconds oddly enough), so it appears that SQLite is indeed ignoring our index unless we force it.
I've also tried some other things to see if they'd make a difference, but they didn't:
DESC
as described here, with and without INDEXED BY
id
column in the index, with and without internaldate_time_t
ordered DESC
, with and without INDEXED BY
Questions
100 milliseconds here still seems awfully slow for what seems like it should be a simple FTS lookup and indexed order.
Thanks!
An index is useful for looking up a table row based on the value of the indexed column. Once a table row is found, indexes are no longer useful because it is not efficient to look up a table row in an index by any other criterium.
An implication of this is that it is not possible to use more than one index for each table accessed in a query.
Also see the documentation: Query Planning, Query Optimizer.
Your first query has the following EXPLAIN QUERY PLAN output:
0 0 0 SCAN TABLE MessageSearchTable VIRTUAL TABLE INDEX 4: (~0 rows)
0 1 1 SEARCH TABLE MessageTable USING INTEGER PRIMARY KEY (rowid=?) (~1 rows)
0 0 0 USE TEMP B-TREE FOR ORDER BY
What happens is that
MessageSearchTable
rows;MessageTable
primary key index is used to find the matching row;Your second query has the following EXPLAIN QUERY PLAN output:
0 0 0 SCAN TABLE MessageTable USING COVERING INDEX MessageTableInternalDateTimeTIndex (~100000 rows)
0 0 0 EXECUTE LIST SUBQUERY 1
1 0 0 SCAN TABLE MessageSearchTable VIRTUAL TABLE INDEX 4: (~0 rows)
What happens is that
MessageSearchTable
rows;MessageTableInternalDateTimeTIndex
in the index order, and returns a row when the id
value is one of the values found in step 1.
SQLite stops after the tenth such row.In this query, it is possible to use the index for (implied) sorting, but only because no other index is used for looking up rows in this table. Using an index in this way implies that SQLite has to go through all entries, instead of lookup up the few rows that match some other condition.
When you omit the INDEXED BY
clause from your second query, you get the following EXPLAIN QUERY PLAN output:
0 0 0 SEARCH TABLE MessageTable USING INTEGER PRIMARY KEY (rowid=?) (~25 rows)
0 0 0 EXECUTE LIST SUBQUERY 1
1 0 0 SCAN TABLE MessageSearchTable VIRTUAL TABLE INDEX 4: (~0 rows)
0 0 0 USE TEMP B-TREE FOR ORDER BY
which is essentially the same as your first query, except that joins and subqueries are handled slightly differently.
With your table structure, it is not really possible to get faster. You are doing three operations:
MessageSearchTable
;MessageTable
;MessageTable
value.As far as indexes are concerned, steps 2 and 3 conflict with each other.
The database has to choose whether to use an index for step 2 (in which case sorting must be done explicitly) or for step 3 (in which case it has to go through all MessageTable
entries).
You could try to return fewer records from the FTS search by making the message time a part of the FTS table and searching only for the last few days (and increasing or dropping the time if you don't get enough results).