Using CTEs to do a binary search of large tables with non-indexed correlated data
Query optimization can take different forms depending on the data represented and the required needs. In a recent case, we had a large table that we had to query for some non-indexed criteria. This table was on an appliance that we were unable to modify, so we had to find a way to query efficiently without indexes that would have made it easier.
The straightforward approach for this query was something along these lines:
SELECT accounts.* FROM accounts JOIN logs ON accounts.id = logs.account_id WHERE logs.created_at BETWEEN $1 - 'interval 1 minute' AND $1 + 'interval 1 minute' AND logs.field1 = $2 AND logs.field2 = $3 FETCH FIRST ROW ONLY
Unfortunately, none of the fields involved in this query were indexed, nor could they be, due to our access level on this database system. This lack of indexes means that our query against those fields would end up doing a sequential scan of the whole table which made things unacceptably slow. This specific table held time-series data with ~ 100k records per 1 minute period over a period of several weeks, which meant we were dealing with a lot of data.
While we could not create any additional indexes to help us with this query, we could use some specific properties to help us:
There was a primary key field,
id, which was unique and monotonic, i.e., always increasing in value.
This table was append only; no updates or deletes, so once data existed in the table it was always the same.
The field we actually care about (
created_at) also ends up being monotonic: subsequent records would always have the same or later values.
Since records were created sequentially and the
idfield was always increasing,
created_atfields would be together be generally monotonic; this means there is an indexed field which we can use as a surrogate stand-in for the target field that we want to treat as an index. While due to the nature of logging ingest it is possible the
idvalues are not strictly monotonic (for instance if there are multiple logging records being created by separate ingest processes, of which
ids get assigned in chunks), for our purposes this was close enough, since we were looking around in a time window which was wider than we were actually expecting the message to appear.
Since we are looking for fields matching a specific window of time, we can substitute the non-indexed clause
created_at BETWEEN <timestamp_min> AND <timestamp_max>with an expression matching the indexed statement
id BETWEEN <id of first id gt timestamp_min> AND <id of first id gt timestamp_max>to get the same effective approach.
In order to find the specific
idfields which match the
created_attime ranges we are interested in, we would need to find the first
idvalue which matched the criteria
created_at > 'timestamp'::timestamp, as all subsequent
idvalues would match that condition as well. This would effectively require a binary search of the table to check which records match the criteria, and return the smallest
idvalue for which this criteria held.
So now that we have identified how we can use an indexed surrogate key to substitute for the non-indexed expression, we need to figure out how to calculate the ranges in question.
Based on some recent discoveries about optimizing simple-looking but poorly-performing queries using
more complicated queries1, I had instincts that this could be solved with a
Common Table Expression. After toying around for a while and not coming up with the exact solution,
I ended up visiting the
#postgresql channel on FreeNode IRC Network. There, I presented the
problem and got some interested responses, as this is the exact kind of question that database
nerds experts love2. The solution that user
xocolatl (Vik Fearing) came up with for a basic
binary search is the basis for the rest of my solution:
CREATE TABLE test_table (id integer PRIMARY KEY, ts timestamptz); INSERT INTO test_table SELECT g, date 'today' + interval '1s' * g FROM generate_series(1, 1000000) AS g; WITH RECURSIVE search (min, max, middle, level) AS ( SELECT min(id), max(id), (min(id)+max(id))/2, 0 FROM test_table UNION ALL SELECT v.min, v.max, (v.min + v.max)/2, s.level+1 FROM search AS s CROSS JOIN LATERAL ( SELECT * FROM test_table AS e WHERE e.id >= s.middle ORDER BY e.id FETCH FIRST ROW ONLY ) AS e CROSS JOIN LATERAL (VALUES ( CASE WHEN e.ts < now() THEN e.id ELSE s.min END, CASE WHEN e.ts < now() THEN s.max ELSE e.id END )) AS v (min, max) WHERE (v.min + v.max)/2 NOT IN (v.min, v.max) ) SELECT * FROM search AS s JOIN test_table AS e ON e.id = s.middle ORDER BY s.level DESC FETCH FIRST ROW ONLY;
As expected, the solution involved a
WITH RECURSIVE CTE.
The basic explanation here is that the
search expression first starts with the
middle (mean) values of
id for the table (the initialization expression), then iteratively adds
additional rows to the results depending on if the table row with
id >= middle matches our
specific test criteria, then continues until one of the boundaries of the region is hit. (Since we
are using integer division in our terminal expression
(v.min + v.max)/2 NOT IN (v.min, v.max) we
are guaranteed to hit one of the boundary conditions eventually in our search.)
A few other things worthy of note:
This approach uses the check
e.ts < now()as the condition we are testing, which means in this specific example, the answer to this “closest id” query would actually change depend on when you run this query relative to when the initial test data was populated. However, we can replace that condition with whatever condition we want to use to test for our surrogate non-indexed data.
This approach will work whether or not there are gaps in the sequence. In order to properly handle gaps, we are selecting the first row with
id >= middle ... FETCH FIRST ROW ONLYrather than just selecting
id = middle, which you could do in a gapless sequence.
In addition to not caring about gaps, we are also not trying to make sure this is using a balanced binary search; it would be not worth the computing effort to find the middlest existing row in an index, as we’d need to know the number of rows in the segment we’re searching. Since in PostgreSQL this would entail a
COUNT(*)from a subselect, this would be quite slow and not worth the trouble.
Considering my specific use case was trying to limit the records considered based on two
created_at values I needed to calculate a
search_max to find the start/end
ids for each side in the interval.
Given this, I just modified the CTE to calculate those and add the additional conditions we were
wanting to consider. I also had to turn the result query from a join against the found
to a range; the final query is as follows:
WITH RECURSIVE search_min (min, max, middle, level) AS ( SELECT min(id), max(id), (min(id)+max(id))/2, 0 FROM logs UNION ALL SELECT v.min, v.max, (v.min + v.max)/2, s.level+1 FROM search_min AS s CROSS JOIN LATERAL ( SELECT * FROM logs AS e WHERE e.id >= s.middle ORDER BY e.id FETCH FIRST ROW ONLY ) AS e CROSS JOIN LATERAL (VALUES ( CASE WHEN extract(epoch FROM e.created_at)::integer < $1 THEN e.id ELSE s.min END, CASE WHEN extract(epoch FROM e.created_at)::integer < $1 THEN s.max ELSE e.id END )) AS v (min, max) WHERE (v.min + v.max)/2 NOT IN (v.min, v.max) ), search_max (min, max, middle, level) AS ( SELECT min(id), max(id), (min(id)+max(id))/2, 0 FROM logs UNION ALL SELECT v.min, v.max, (v.min + v.max)/2, s.level+1 FROM search_max AS s CROSS JOIN LATERAL ( SELECT * FROM logs AS e WHERE e.id >= s.middle ORDER BY e.id FETCH FIRST ROW ONLY ) AS e CROSS JOIN LATERAL (VALUES ( CASE WHEN extract(epoch FROM e.created_at)::integer < $2 THEN e.id ELSE s.min END, CASE WHEN extract(epoch FROM e.created_at)::integer < $2 THEN s.max ELSE e.id END )) AS v (min, max) WHERE (v.min + v.max)/2 NOT IN (v.min, v.max) ) SELECT accounts.* FROM accounts JOIN logs ON logs.account_id = accounts.id WHERE logs.field1 = $3 AND logs.field2 = $4 AND logs.id >= (SELECT middle FROM search_min ORDER BY level DESC FETCH FIRST ROW ONLY) AND logs.id <= (SELECT middle FROM search_max ORDER BY level DESC FETCH FIRST ROW ONLY) ORDER BY logs.id FETCH FIRST ROW ONLY
The final results were drastically improved. The initial query went from timing out in the webservice in question to returning results in a fraction of a second. Clearly this technique, while not as useful as directly indexing data we care about, can come in handy in some circumstances.
Another tool for the toolbag!